The landscape and regulatory potential of eccDNAs in mammalian preimplantation embryos

Domain: biology
Published: 2026-03-31
Editorial outcome: major_revision

Abstract
Extrachromosomal circular DNA is an emerging regulatory element implicated in genomic stability and gene regulation, yet its role in preimplantation development remains elusive. Here, we report the widespread presence of extrachromosomal circular DNA in preimplantation embryos, characterized by homologous junction sequences and originating from genomic regions enriched for active histone marks and RNA Polymerase II occupancy. Functional perturbations demonstrate that RNA Polymerase II inhibition suppresses extrachromosomal circular DNA production, whereas disruption of the Fanconi anemia pathway elevates it, suggesting that transcription-replication conflicts affect its biogenesis. Notably, extrachromosomal circular DNA levels surge during major zygotic genome activation. Synthetic extrachromosomal circular DNAs carrying putative enhancers for the zygotic genome activation genes Mycn and Egfl7, and the developmental gene Exm1, significantly upregulate the expression of their respective genes upon transfection into fibroblasts and zygotes. Collectively, this study unveils the extrachromosomal circular DNA landscape in preimplantation embryos, elucidates a transcription-replication conflict mechanism underlying its generation, and establishes its regulatory potential during mammalian preimplantation development.

Introduction
Extrachromosomal circular DNA (eccDNA), lacking centromeres and telomeres, is a circular DNA element outside the chromosomes that ranges in size from tens to millions of base pairs1, 2, 3. Large circular DNA molecules spanning hundreds of kilobases to megabases are commonly referred to as extrachromosomal DNA (ecDNA), whereas smaller circular DNA species, typically ranging from tens to several kilobases, are generally termed eccDNA or microDNA4. Across various cellular environments, researchers have thoroughly investigated the genomic origins of eccDNA, revealing CpG islands, gene-dense regions, and repetitive elements like long terminal repeats (LTRs), Long interspersed nuclear element-1 (LINE-1), segmental duplications, and satellite DNA as prime sites for eccDNA formation5, 6, 7, 8. In epigenomic studies, higher GC content and dinucleotide patterns suggest that nucleosome packaging may influence the formation of eccDNA molecules5, 9. However, some studies present contrasting findings, reporting a near-random distribution10, 11.

Considering the dynamic and varied nature of eccDNA formation, it seems that DNA damage serves as the primary underlying factor. Several distinct mechanisms have been hypothesized, including the breakage-fusion-bridge (BFB) cycle, translocation deletion amplification processes, episome model, chromothripsis, repeat-based mechanisms, replication slippage, and the fork stalling and template switching (FoSTeS) phenomena4. Based on these comprehensive theories, eccDNA is linked to apoptotic events, arising from the stochastic ligation of genomic DNA fragments9. Central to these processes are the canonical DNA double-strand breaks (DSBs) repair pathways, such as homologous recombination (HR) and microhomology-mediated end joining (MMEJ)5, 6, 12. In addition, researchers have also recognized a comparable repair mechanism to MMEJ, termed alternative end-joining (alt-EJ), which is associated with the biosynthesis of eccDNA derived from retrotransposons in Drosophila13.

The role of large, megabase-sized ecDNA in cancer biology is well-established. These molecules exhibit highly open chromatin structures that potently activate the transcription of carried oncogenes14. This, in turn, drives tumor progression by promoting oncogene overexpression, enhancing genomic heterogeneity, and enabling drug resistance through gene amplification15, 16, 17, 18, 19, 20. In parallel, a distinct population of small eccDNAs has been identified in both normal biological and pathological conditions8, 21. These molecules play diverse roles, including regulating gene expression, participating in DNA damage repair mechanisms12, and serving as potential biomarkers for disease detection and monitoring22, 23. Notably, these small species are abundantly generated in the germline, as evidenced by their extensive profiles in both humans and mice11, 24.

Early mammalian embryonic development begins with fertilization, which is the fusion of two terminally differentiated gametes: oocytes and sperm. The hallmarks of early embryonic development in most species include a series of cleavage divisions with distinctive cell-cycle dynamics, together with the transition from maternal to zygotic control of gene expression. Following fertilization, the genome within the totipotent embryo remains transcriptionally silent, and then initiates the comprehensive transcriptional activation, known as zygotic genome activation (ZGA), which is the first transcriptional event following fertilization in mammals. In mice, ZGA can be divided into two stages: a minor wave of ZGA occurs within 12 hours after fertilization in late one-cell embryos, and a major wave of ZGA, occurring during the mid to late 2-Cell stage25, 26, 27. Early embryonic development is tightly regulated by genetics and epigenetics. Due to drastic and extensive epigenetic reprogramming, early mammalian embryos can undergo extensive DNA damage and repair during the preimplantation stage28, 29. The above process should be accompanied by the production of a substantial amount of eccDNA. However, the presence of eccDNA in mammalian preimplantation embryos, as well as its dynamic changes during this developmental window, has not been thoroughly investigated. Further studies are needed to determine whether there is crosstalk between the generation of eccDNA and key developmental processes such as ZGA and lineage differentiation during embryonic development.

In the current study, we comprehensively characterized the eccDNA landscape during mammalian preimplantation development through an integrated analysis of multiple displacement amplification (MDA)-based whole genome sequencing (WGS), ATAC-seq, RNA-seq, ChIP-seq, and single-cell Repli-seq datasets30. Our analysis indicates that eccDNA participated in the ZGA process and modulated gene expression during preimplantation embryos. Additionally, our findings indicate a correlation between the formation of eccDNA and transcription-replication conflicts (TRCs), offering insights into the underlying mechanisms that may be pivotal to eccDNA formation.

Results
The widespread presence of eccDNA in mouse preimplantation embryos
Circular DNA can be computationally inferred from whole genome sequencing (WGS) data through the analysis of unique split reads and discordant reads31. To generate such data, we carried out extensive genomic sequencing using MDA on IVF-derived embryos across six pivotal stages of mouse preimplantation development: zygote, 2-Cell, 4-Cell, 8-Cell, morula and blastocyst stages. Then we applied the Circle_finder pipeline31 to investigate the characteristics of genome-wide eccDNA during mouse preimplantation embryos based on our WGS sequencing data (Figure 1A). Due to the limitation of the Circle_finder pipeline in distinguishing between extrachromosomal circles and chromosomal segmental tandem duplications31, 32, we implemented a repeat-masked genome (see Methods) to ensure the credibility of detected eccDNA. The number of eccDNAs exhibiting complete overlap between the two replicates at each stage is shown in Supplementary Figure 1A and Supplementary Data 1. For a more in-depth exploration of this eccDNA distribution, we combined the raw data from replicate samples of each embryonic stage for thorough analysis. The results indicated that a substantial amount of eccDNA was generated during mouse preimplantation development (Supplementary Data 2). Surprisingly, we found that the amount of eccDNA was highest at the 2-Cell stage, which is a prerequisite and key event for successful preimplantation embryo development33. The large amount of eccDNA produced at the 2-Cell stage might be closely related to the occurrence of ZGA, followed by a subsequent gradual decrease until the morula stage (Figure 1B). In total, 399,660 unique eccDNAs (Supplementary Data 2) were identified across the six distinct stages, with 104,557 eccDNAs newly generated during the 2-Cell stage compared to zygote stage (Supplementary Figure 1B, left). To investigate the potential link between eccDNA biogenesis and ZGA, we analyzed the association between genes harboring newly generated eccDNAs at the 2-Cell stage and previously reported ZGA genes34. This analysis revealed a significant overlap of 1,229 genes (Supplementary Figure 1B, right; Supplementary Data 3). To assess the statistical significance of this overlap, we performed control analyses by randomly selecting an equivalent number of genes from both the entire mouse genome and a non-ZGA gene set. Fisher's exact test demonstrated a highly significant overlap of 1,229 genes between eccDNA-associated and ZGA gene sets (P value < 0.001; Supplementary Figure 1B, right), which was not observed in random controls (Supplementary Figure 1C). This confirms a non-random, highly specific association between eccDNA formation and ZGA. Notably, among the eccDNAs newly generated at the 2-Cell stage, several were located in key ZGA genes such as Pramel5, Ntrk2, and Egfl7. We further validated several ZGA-associated eccDNA candidates from the 2-Cell stage using reverse PCR and Sanger sequencing. These eccDNAs were located in promoter regions, specifically chr4:144,257,675–144,304,941, chr13:58,830,714–58,839,621, and chr2:26,593,442–26,594,001, which we predicted to be associated with Pramel5, Ntrk2, and Egfl7, respectively. Additional eccDNAs were located in intronic (chr13:106,824,086–106,828,831), distal intergenic (chr1:99,465,395–99,467,467), chr8:115,600,974–115,601,341, chr12:13,032,545–13,032,927, and 5′ UTR regions (chr10:82,876,168–82,877,275), which were predicted to be associated with Ipo11, Klf5, Maf, Mycn, and Eid3 (Supplementary Data 4; Supplementary Figure 1D; Figure 5E). These findings suggest a significant presence of ZGA-associated eccDNA in mouse 2-Cell embryos, implying a strong connection between eccDNA generation and ZGA processes. Given the lack of prior reports on eccDNA in preimplantation embryos, our subsequent focus was on analyzing its genomic features, sequences, and possible functions, providing valuable insights for future research endeavors.

Characteristics of eccDNA in mouse preimplantation embryos
Initially, we confirmed that the size distribution of eccDNA from various embryos follows a consistent pattern (Figure 1C). Across all stages, there was a notable peak percentage of eccDNA in the 100-10,000 bp range, with percentages decreasing as the length increased. This trend suggests that shorter eccDNA molecules are more abundant during these embryonic stages. Subsequently, the distribution of eccDNA per megabase across each chromosome was examined (Supplementary Figure 1E), revealing that chromosomes 16 and 6 exhibit the highest eccDNA density. Genome-scale map analysis indicated that eccDNA is predominantly distributed in distal intergenic, introns and promoter regions (Supplementary Figure 1F). Next, we evaluated potential correlations between eccDNA frequency and genomic features. No correlation was identified between eccDNA and gene density (Supplementary Figure 1G, left; R = 0.099, P value = 0.67). A moderate positive correlation was observed between eccDNA and Alu element density although it was not statistically significant (Supplementary Figure 1G, right; R = 0.402, P value = 0.071). Previous studies have reported variable relationships between eccDNA frequency and genomic features such as gene density and Alu element content8, 11. In our dataset from early preimplantation embryos, eccDNA frequency did not exhibit a significant correlation with these genomic features. These differences are likely attributable to cell-type-specific mechanisms of eccDNA formation and to differences in genomic hotspots of eccDNA generation.

Through a comparative analysis of eccDNA dynamics between two consecutive stages (see Methods), we observed a continuous generation of novel eccDNA along with the retention of eccDNA from earlier developmental stages. Approximately 3,500 stable eccDNAs were persistent between the two stages, indicating a degree of stability in the eccDNA landscape (Figure 1D). Nevertheless, substantial stage specific differences were also observed, likely reflecting the distinct biological processes regulating eccDNA generation at each developmental stage. Notably, during the transitions from the zygote to the 2-Cell stage and subsequently to the 4-Cell stage, eccDNA exhibited significant dynamic changes, characterized by marked production and reduction (Figure 1D). These changes may reflect regulatory mechanisms associated with ZGA. The intersection of eccDNA across all developmental stages revealed 2,285 eccDNAs that were consistently present throughout, which we refer to as consistent eccDNAs. The number of these consistent eccDNAs was relatively small compared to stage-specific eccDNAs (Supplementary Figure 2B). They tended to be longer than eccDNAs from individual stages (Figure 1E, and Supplementary Figure 2C), and were predominantly enriched in promoter regions relative to the overall eccDNA distribution across stages (Figure 1F and Supplementary Figure 1F). Notably, a recent study35 reported a higher frequency of DSBs in promoter regions, suggesting a potential mechanistic link to eccDNA generation. Functional enrichment analysis using the GREAT tool revealed that genes associated with these consistent eccDNAs were significantly enriched in biological pathways related to G-protein coupled receptor signaling and insulin-activated receptor activity (Figure 1G). Taken together, these observations suggest that consistent eccDNAs may be preferentially associated with regulatory genomic regions and signaling-related genes, thereby potentially contributing to transcriptional regulatory landscapes during preimplantation development.

Formation mechanisms of eccDNA during mouse preimplantation stages
Understanding the characteristics of eccDNA junction sites is crucial, as it provides direct insights into the mechanisms of circularization and identifies the genomic regions susceptible to eccDNA formation5, 36. For each detected eccDNA, we analyzed the 20 bp upstream and downstream sequences from both the start and end points, labeling them as Sequences I and II according to their genomic orientations (Figure 2A). Our analysis revealed that both I and II Sequences contained 2- to 40-base direct repeats (Supplementary Data 5). We identified direct repeats of more than 7 bp around junction sites in approximately 30% of eccDNAs (Figure 2B), in contrast to only ~9% in simulated control sequences (Supplementary Figure 2A). Additionally, the length of these direct repeats positively correlated with the size of the eccDNA (Figure 2C). Consistently, it has been demonstrated that longer DNA fragments exhibit increased spatial separation between free ends, necessitating longer homologous sequences to effectively facilitate the process of recombination24. Similarly, several studies have also proposed that the presence of microhomology at eccDNA junctions suggests that MMEJ may contribute to the circularization of eccDNA6, 24, 36. We found that several TF motifs, including GATA4, FOXO1, and SOX15 enriched in both Sequences I and II at the 2-Cell stage (Figure 2D). These TFs exhibit high transcription levels and translation efficiency during the mouse 2-Cell stage, playing crucial roles in ZGA and early development37. Studies have shown that GATA4 binding sites are enriched in active histone marks38, and GATA4 has been demonstrated to promote chromatin decompaction and nucleosome eviction39. Similarly, FOXA/FOXO family members are classified as pioneer TFs that bind nucleosomal DNA and promote chromatin opening40, 41. These TFs binding regions are associated with high transcriptional activity and increased topological stress, making them more susceptible to DNA breakage. Therefore, the presence of TF motifs at eccDNA junctions could reflect both regulatory activity and DNA fragility.

Previous research has indicated that microDNA from adult mouse tissues tends to be enriched at promoters with activating or bivalent markers, as well as regions occupied by RNA Polymerase II (RNA Pol II)7. Additionally, the strong association of eccDNA in sperm with euchromatin24 prompted us to investigate the relationship between eccDNA and transcriptionally active histone modifications, such as H3K27ac and H3K4me3. Reanalyzing public ChIP-seq data for H3K27ac, H3K4me3, and Pol II in mouse embryos42, 43, 44, 45, 46, we found that across all analyzed stages of preimplantation development, eccDNA regions exhibited significantly higher enrichment of active histone modifications H3K27ac and H3K4me3 compared with control regions (Figure 2E, F), as well as stronger Pol II signals relative to controls (Figure 2G). We then categorized the reads into different types, including split reads, discordant reads, and concordant reads, and found that these two active histone modifications and Pol II binding signal were most enriched in split and discordant reads (Supplementary Figure 2D), suggesting that these active transcription markers do indeed originate from eccDNA. We further analyzed the GC content of the regions surrounding eccDNA junction sites across developmental stages (Supplementary Figure 2E). We found that the GC content in these regions ranges from 0.39 to 0.41, indicating an AT-rich characteristic. Notably, the 2-Cell stage showed the lowest GC content, suggesting a higher AT content at this stage. Previous studies have shown that AT-rich regions tend to form open chromatin structures and are associated with nucleosome-free regions and transcriptional activation47. Therefore, we speculate that eccDNAs preferentially arise from such AT-rich regions, which may exhibit a more open and flexible chromatin configuration, making them more susceptible to DNA breakage and subsequent circularization.

The initial DNA replication in the zygote is essential for triggering mitosis in the preimplantation embryo. This process follows a specific temporal sequence known as the replication timing (RT) program, dividing the genome into early and late replicating regions30. By utilizing the most recent genome-wide replication timing atlas of mouse embryos provided by Nakatani et al.30, we delved into the complex replication patterns during early embryos. Our analysis revealed that regions overlapped with eccDNA exhibited notably higher RT values in early stages compared to those without eccDNA overlapping, indicating their robust replication activity (Figure 2H). However, the RT values of eccDNA regions declined by the morula stage, suggesting a diversification of the sources contributing to eccDNA as development progresses.

Suppression of transcription led to a marked decrease in both the quantity and abundance of eccDNA
To further investigate the influence of transcription on the production of eccDNA, we utilized α-amanitin to inhibit RNA Pol II-mediated transcriptional activity in zygote (Supplementary Figure 3A), and then performed MDA-based WGS and RNA-seq experiments to systematically compare their genomic and transcriptomic landscapes between the control and the blocked embryos (Figure 3A). Firstly, we analyzed transcriptome data to determine whether inhibiting transcription affected ZGA. A strong correlation was observed between the two biological replicates within each group’s RNA-seq samples, underscoring the reliability and accuracy of the gene expression data obtained (Supplementary Figure 3B). Our differential expressed gene (DEG) analysis revealed 4,269 DEGs (P ≤ 0.05; fold change ≥ 2) between the blockage and control group, with 491 genes showing upregulation and 3,778 genes displaying downregulation (Figure 3B; Supplementary Data 6). To further explore these changes, we compared them with previously reported ZGA genes and found that 1,399 ZGA genes were downregulated (Figure 3C), confirming that the transcriptional block impacted genes crucial for the normal progression of ZGA in embryos. In terms of eccDNA, we found a significant decrease of eccDNA quantity in the inhibition group (Figure 3D). To further explore the dynamic changes of eccDNA, we developed a pipeline to quantify eccDNA based on its relative abundance (Supplementary Figure 3C) (see Methods). Our analysis revealed a significant downregulation of a large amount of eccDNA (n=4,607) following inhibition (Figure 3E). These results indicate that the quantity and abundance of eccDNA were both significantly reduced after transcriptional inhibition with α-amanitin. Next, we explored the relationship between downregulated genes and reduced eccDNA by joint analysis of eccDNA and RNA-seq data. We observed that the reduction of eccDNA abundance was obviously correlated with the downregulation of their associated genes (Figure 3F). Regard of key ZGA genes, we further confirmed that the reduction in eccDNA abundance notably occurred in regions associated with repressed ZGA genes, such as Btg3L, Ccdc126, Mycn, and Sox11 (Supplementary Figure 3D; Supplementary Data 7). To investigate the relationship between transcriptional repression and eccDNA reduction, we compare genes that were downregulated at the RNA-seq level with those associated with decreased eccDNA signals. This analysis revealed a highly significant overlap between the two gene sets (P value < 2.2 × 10-16) (Figure 3G), indicating a strong association between eccDNA loss and gene downregulation. In contrast, a control analysis using randomly selected gene sets of the same size from the genome showed no significant overlap with the RNA-seq downregulated genes (P value = 0.47689) (Supplementary Figure 3E), highlighting the specificity of the observed relationship. Then we performed gene ontology (GO) enrichment analysis on the intersecting genes. The top 20 enriched pathways were closely associated with the ZGA process, including RNA metabolic processes and nucleic acid metabolic processes (Figure 3H), suggesting that eccDNA-associated transcriptional changes may play a pivotal role in regulating early preimplantation development.

Increased TRCs lead to elevated levels of eccDNA, both in terms of its quantity and abundance
Given the established role of the Fanconi anemia (FA) pathway in resolving TRCs48, we aimed to investigate the functional connection between TRCs and eccDNA biogenesis in preimplantation embryos by disrupting the FA pathway. To this end, we treated zygotes with the FA pathway inhibitor ML323 at varying concentrations. ML323 impaired mouse preimplantation development in a concentration-dependent manner: exposure to 50 µM significantly reduced developmental rates across embryonic stages, whereas 100 µM led to widespread arrest at the zygote stage (Figure 4A). Consistent with this phenotype, the proportion of developmentally arrested embryos was significantly higher in ML323-treated groups than in DMSO controls (Figure 4B). To confirm the on-target effect of ML323, we treated zygotes with 50 µM ML323 for 8 hours and performed immunofluorescence staining for γH2A.X, a well-established marker of DNA damage. We observed a marked accumulation of γH2A.X foci in both pronuclei (Figure 4C), indicating the accumulation of unresolved DNA damage, which is a hallmark of TRCs, and consequently validating the effective impairment of the FA pathway. Following MDA-based WGS of both 50 µM ML323-treated and control 2-Cell stage embryos, we observed an overall increase in both the quantity and abundance of eccDNA in the treated group. Although the average number of eccDNA per sample did not change significantly (Figure 4D), a subset of eccDNAs was significantly upregulated (Figure 4E). Gene association analysis revealed that these upregulated eccDNAs were significantly enriched in ZGA-related genes, with 155 genes overlapping a known ZGA gene set34 (Figure 4F). To rule out the possibility that this overlap occurred by chance, we performed a randomization test by sampling an equal number of genes 1000 times. The mean number of overlapping genes with the ZGA set in these random trials was significantly lower than 155 (Figure 4G), confirming that the observed enrichment is biologically meaningful. These results suggest that FA pathway inhibition exacerbates TRCs, thereby elevating eccDNA production, which in turn may disrupt the regulation of ZGA.

Potential roles of eccDNA during preimplantation development
To study the characteristics and potential biological functions of eccDNA, we quantified all eccDNA based on its relative abundance (see Methods). Principal components analysis (PCA) revealed that embryos at similar developmental stages clustered closely, whereas those at different stages separated as expected, demonstrating high data reproducibility (Figure 5A, upper, Supplementary Figure 4A). To systematically analyze the associations between the dynamic changes of eccDNA and RNA expression, we collected RNA-seq datasets from preimplantation developmental stages43, 49. Spearman’s correlation analysis showed a similar pattern with intra-group similarity surpassing inter-group similarity (Supplementary Figure 4B). In contrast to the dynamic changes of eccDNA, PCA of RNA-seq data revealed a continuum of variation during mouse preimplantation development (Figure 5A). Both the transcriptome and eccDNA atlas diverged significantly from the 2-Cell stage, coinciding with the occurrence of ZGA. These findings imply a synergistic regulatory interplay between eccDNA dynamics and gene expression. Next, we utilized the fuzzy c-means algorithm50 to delineate the profiles of eccDNA dynamic changes across all developmental stages. Our analysis revealed six distinct clusters, each representing unique temporal patterns of eccDNA with diverse regulatory dynamics. The heatmap demonstrated that distinct clusters of eccDNA enrichment were present across each of the six developmental stages. (Figure 5B; Supplementary Data 8). To better understand the functional relevance of these clusters, we conducted GO enrichment analysis for each cluster. The enrichment analysis of eccDNA-associated genes in each module revealed comparable biological functions, including RNA Pol II transcription, RNA metabolic processes, reproduction, and cell differentiation (Figure 5C), aligning with pivotal biological processes during embryonic development. Notably, Cluster 1 showed activity in cellular differentiation processes, suggesting enrichment for cell development consistent with the meiotic-to-mitotic transition characteristic of the zygote (Figure 5C). In terms of Cluster 2, the associated genes in this module showed enrichment in the regulation of transcription by RNA Pol II and reproduction (Figure 5C), manifesting significant transcription activity during ZGA. Moreover, a significant overlap of 569 genes was observed between eccDNA-associated genes in Cluster 2 and known ZGA genes (P value < 2.2 × 10-¹⁶; Figure 5D). In contrast, control analyses using randomly selected gene sets of the same size from either the whole genome (P value = 0.52937; Supplementary Figure 4C) or from non-ZGA genes (P value = 0.67519; Supplementary Figure 4D) showed no significant enrichment, underscoring the specificity of the observed association. Furthermore, several eccDNAs corresponding to genes within this overlapping set were randomly selected and experimentally validated (Figure 5E). These results suggest that eccDNAs present specifically at the 2-Cell stage are preferentially associated with ZGA-related genes. eccDNA in Cluster 3 was highly expressed in 4-Cell embryos, and showed enrichment of cellular developmental process, protein localization, and regulation of nucleic acid-templated transcription (Figure 5C). Corresponding genes in Cluster 4 showed high activity in 8-Cell embryos and were enriched for the establishment of localization and nucleic acid metabolic processes (Figure 5C). These findings enhanced our understanding of eccDNA dynamics during preimplantation development and link eccDNA to key developmental genes, such as Rfpl4b and Zfp352 in the 2-Cell stage, Tspan1, Xist, Sall1, Tbx20, Pax6 and Gjb3 in the 8-Cell stage, and Zfp51 in the blastocyst stage 43, 51. Taken together, the above analyses revealed dynamic changes of eccDNA during mouse preimplantation development, and the eccDNA dynamics at distinct stages could potentially serve as indicators of lineage specification.

Although eccDNAs have been shown to influence gene expression in certain pathological contexts such as cancer, their functional relevance during preimplantation development remains poorly understood. To explore the potential regulatory role of eccDNAs, we first compared the expression levels of eccDNA-associated genes to those of all other genes across developmental stages. We observed a significantly higher expression of eccDNA-associated genes (Figure 6A), suggesting that eccDNAs may contribute to transcriptional activation during preimplantation development. To further investigate this possibility, we analyzed the correlation between eccDNA abundance and gene expression using Spearman’s correlation coefficient across developmental stages. Genes whose expression levels positively correlated with their corresponding eccDNAs were defined as eccDNA+ genes, but as eccDNA- genes otherwise. This analysis identified 1,230 eccDNA+ genes and 610 eccDNA- genes consistently across all stages (Figure 6B). GO analysis revealed that eccDNA- genes were primarily enriched in biological processes such as negative regulation of cellular processes, establishment of localization, and negative regulation of metabolic process (Figure 6C, top). In contrast, eccDNA+ genes were significantly enriched in pathways essential for ZGA, including positive regulation of cell differentiation, MAPK signaling, and RNA polymerase II-mediated transcription (Figure 6C, bottom). Importantly, eccDNA+ genes showed significant overlap with known ZGA genes34 (P value = 6 × 10-5; Figure 6D), whereas random gene sets of the same size drawn from the genome showed no such enrichment (P value = 0.7681; Supplementary Figure 4E). These overlapping genes, hereafter referred to as eccDNA+ ZGA genes, exhibited markedly elevated expression specifically at the 2-Cell stage compared to all other developmental stages (Figure 6E). To determine whether this expression pattern was attributable solely to ZGA dynamics, we performed a parallel analysis using eccDNA- ZGA genes, defined as ZGA genes overlapping with eccDNA- genes. Unlike eccDNA+ ZGA genes, the eccDNA- ZGA genes only exhibited significantly increased expression at the 2-Cell stage relative to the zygote stage, but not when compared to later stages (Figure 6F). This distinction supports a specific association between eccDNA and enhanced transcription of a subset of ZGA genes, suggesting that eccDNA may play a role in fine-tuning gene expression during genome activation in preimplantation development.

Based on the above findings, we next asked whether eccDNA could enhance gene expression during preimplantation development. To test this, we synthesized eccDNA molecules eccMycn, eccEgfl7, and eccExm1 using Ligation-Assisted Minicircle Amplification (LAMA)52 (Supplementary Figure 4F–G and Figure 6G–H). These constructs carry potent enhancers of the ZGA genes Mycn and Egfl7, as well as the developmental gene Exm1, identified in our initial mapping. The eccDNAs were then transfected into mouse embryonic fibroblast cell line NIH 3T3 and/or zygote. We found that the mRNA levels of these genes were significantly higher than those in the control group (Figure 6I-J, Supplementary Figure 4H). Together, these results suggest that eccDNA contributes to ZGA and may play a broader role in regulating gene expression during preimplantation development.

Collectively, through a dynamic analysis of eccDNAs in mouse preimplantation embryos, we provide a comprehensive landscape, propose potential mechanisms for their biogenesis, and suggest their possible regulatory functions, as summarized in our model (Figure 6K). As detailed in this study, embryonic eccDNAs are enriched in regions marked by active transcription and replication signals, indicating hotspots for both processes. The frequent collisions between transcription and replication machineries are known to cause genomic instability, including R-loop formation and DNA damage53, 54, 55, which have been proposed as potential triggers for eccDNA biogenesis in other systems56. However, the precise molecular pathways leading from TRCs to eccDNA formation in preimplantation embryos remain to be fully elucidated. Multiple replication-associated mechanisms, including fork stalling, template switching, and replication slippage, may underlie the generation of eccDNAs from these genomic regions7, 57, 58. Importantly, while error-prone repair of DNA double-strand breaks (e.g., via NHEJ, MMEJ, or SSA) can generate eccDNAs, such events often cause insertions, deletions, or loss of heterozygosity (LOH)59, 60. Given that large-scale LOH is highly deleterious in early embryos, it is unlikely to be a major source of the eccDNAs observed in healthy development. Taken together, we propose a working model wherein eccDNAs during preimplantation development arise primarily from replication-associated processes triggered by transcription-replication conflicts, without necessarily invoking catastrophic LOH. To elucidate the exact mechanisms (such as R-loop mediation and replication slippage) operating during preimplantation development will be an important goal for future research.

Characteristics of eccDNAs during preimplantation development are conserved between human and mouse
Researchers have validated that ATAC-seq experiment is a feasible and sensitive method for detecting eccDNA31. We also collected public ATAC-seq datasets covering key stages of mouse preimplantation development to identify eccDNA (Figure 7A). The collected stages ranged from early cleavage (2-Cell, 4-Cell, 8-Cell) to the blastocyst stage, represented by the inner cell mass (ICM), and also included embryonic stem cells (ESCs) derived from the ICM. A total of 20,384 eccDNAs were detected (Supplementary Figure 5A; Supplementary Data 9). We found that the quantity of eccDNA reaches its peak in the ICM cells of the blastocyst stage, which corresponds to the ICM undergoing rapid cell divisions and cell fate decisions61. To verify the robustness of our eccDNA identification pipeline from ATAC-seq data, we directly confirmed the presence of eccDNA in mouse 2-Cell and 4-Cell embryos using reverse PCR (Supplementary Figure 5B, 5C). These eccDNAs had a large specificity (Supplementary Figure 5D) with only eight eccDNAs overlapping across all stages. The sizes of eccDNA varied from tens of bases to tens of megabases, with the majority of them being around ~1,000 bp (Supplementary Figure 5E). The chromosomal distribution of eccDNA revealed the presence of eccDNA sequences originating from all chromosomes (Supplementary Figure 5F). The eccDNA detected in the ATAC-seq data was primarily distributed in the promoter region (Supplementary Figure 5G), displaying a different pattern compared to the eccDNA mainly located in the distal intergenic region identified in WGS data (Supplementary Figure 1F). The difference can be attributed to experimental technique, as ATAC-seq specifically aims to enrich open chromatin regions in the genome. Furthermore, H3K27ac, H3K4me3, and Pol II signals were highly enriched within eccDNA regions identified from the ATAC-seq dataset (Supplementary Figure 5H). Together, these results indicated that the identification of eccDNA in ATAC-seq datasets was reliable, and the characteristics of these eccDNAs were consistent with our observations from WGS experimental datasets.

Due to ethical constraints and challenges in obtaining human embryos, we further demonstrated the widespread presence of eccDNA during human preimplantation development by analyzing public ATAC-seq datasets (Figure 7A). The analyzed stages included the zygote, 2-Cell, 4-Cell, and 8-Cell stages, as well as the blastocyst stage represented by the inner cell mass (ICM) and its derivative embryonic stem cells (ESCs). Overall, we detected a total of 5,475 eccDNAs in human embryos, underscoring the broad distribution of eccDNA during the initial stages of human development (Figure 7B; Supplementary Data 10). The highest abundance of eccDNA was observed in ICM cells at the blastocyst stage, consistent with the above analysis in mouse. Regarding their sizes, eccDNA exhibited a diverse range, varying from tens of bases to tens of megabases, with the majority falling around the 1,000 bp mark (Figure 7C). Notably, chromosomes 17 and 19 exhibit relatively high eccDNA density (Supplementary Figure 5I). The distribution of eccDNA in the designated genomic elements is consistent with that obtained from mouse ATAC-seq data (Figure 7D).

Human developmental disorders, such as autism spectrum disorder (ASD), are strongly influenced by genetic and epigenetic perturbations62. Numerous large-scale sequencing studies have demonstrated that de novo mutations (DNMs) play a major role in ASD etiology63. Meanwhile, eccDNA has been increasingly recognized as an important contributor to gene regulation and chromatin organization, with potential impacts on genome stability and cellular processes1, 8. To investigate whether eccDNAs may intersect with genomic regions relevant to neurodevelopmental pathogenesis, we analyzed the overlap between eccDNA regions and DNMs implicated in ASD. DNMs associated with ASD were obtained from the Gene4Denovo database64. Our analysis revealed that ASD-related DNMs significantly overlapped with eccDNA identified in human preimplantation embryos, compared with simulated random controls (Figure 7E). Although this observation suggests a non-random spatial association between eccDNA-forming regions and DNMs involved in neurodevelopmental disorders, the eccDNAs analyzed in this section were identified based on ATAC-seq data, which inherently biases detection toward open chromatin regions enriched for gene activity. Additionally, eccDNA populations are highly heterogeneous across developmental stages. Further investigation using stage-specific eccDNA datasets and functional validation will be essential to determine whether eccDNA formation contributes to the etiology of human developmental disorders.

eccDNA from human ATAC-seq datasets also exhibited remarkable specificity, with only three overlapping sequences detected (Figure 7F). Similarly, analysis of active histone modifications during human preimplantation stages, including H3K27ac and H3K4me3, showed elevated signals over these regions (Figure 7G).

These findings suggest a widespread presence of eccDNA during mammalian embryonic development. The uniformity in characteristics and distribution patterns of eccDNA in both human and murine systems strongly supports the hypothesis that eccDNA may serve crucial physiological roles during mammalian preimplantation embryonic development.

Methods
Animals and Ethics
All animal experiments were conducted in accordance with relevant ethical regulations and were approved by the Institutional Animal Welfare and Ethics Committee of Peking University Third Hospital (approval number A2024145).

Five-week-old female mice and three-months-old male mice (C57BL/6JCnc, B6J) were provided by Department of Laboratory Animal Science, Peking University Health Science Center (Beijing, China). Mice were reared in a temperature-controlled room (a 12-hours light–dark period), with a temperature of 18-23 ℃ and 40-60% humidity, with food and water ad libitum under specific pathogen-free condition.

To ensure sufficient embryos for all experimental stages and replicates, approximately 140 mice were used in total, including 70 females and 70 males. Each breeding pair yields ~20 gametes or embryos. For this study, we planned to collect 100 gametes/embryos per stage (oocyte, zygote, 2-Cell, 4-Cell, 8-Cell, morula, blastocyst), with two experimental replicates, totaling 1,400 gametes/embryos. This number provides adequate biological replicates and ensures strain stability. Sex was considered in the study design (oocytes were collected from female mice and sperm from male mice), but individual experimental data were not disaggregated by sex.

Following the completion of experiments, mice were euthanized by cervical dislocation, and carcasses were disposed of in designated biomedical waste containers, in accordance with institutional guidelines.

Oocyte collection, in vitro fertilization and embryo culture
For the collection of ovulated oocytes, five-week-old C57BL/6J female mice were intraperitoneally injected with 5 IU pregnant mare’s serum gonadotropin (PMSG, Ningbo Second Hormone Factory, China) followed by 5 IU human chorionic gonadotropin (hCG, Ningbo Second Hormone Factory, China) 46-48 hours later. The MII oocytes were collected from oviducts 14-16 hours after hCG injection and then digested away from the cumulus with 0.3 mg/ml bovine testicular hyaluronidase in M2 medium for 1 minute. Ovulated oocytes were then transferred into fresh M2 medium (Sigma-Aldrich, M7167) at 37°C in 5% CO2.

Sperm was released from the cauda epididymides of 3 months old male mice with proven fertility in human tubal fluid (HTF, Millipore, MR-070-D) medium in oil and capacitated for 1 hour at 37°C in 5% CO2. Ovulated MII oocytes harvested as described above were then incubated in 250 μL HTF with 5.14 mM calcium containing capacitated sperm (～4 × 105/mL) for 6-8 hours at 37°C in a humidified atmosphere of 5% CO2. Upon fertilization, fertilized zygotes with clear pronuclei were transferred into fresh KSOM (Millipore, MR-106-D) medium for in vitro culture. Embryos at different stages were counted and collected at 40 hours (2-Cell stage), 54 hours (4-Cell stage), 72 hours (8-Cell stage), 84 hours (morula) and 96 hours (blastocyst) post hCG injection. Images were captured using an Axio Observer 3 microscope (Zeiss) with ZEN Blue Lite imaging software.

Multiple displacement amplification (MDA), library construction and sequencing
Following the manufacturer’s recommendation, whole metagenome amplification was performed on 6 samples (zygote, 2-Cell, 4-Cell, 8-Cell, morula and blastocyst) using MDA with the REPLI-g Single Cell kit (Qiagen, 150345). Briefly, for cell lysis and lysed genomic DNA (gDNA) denaturation, 4 µL cell material (supplied with PBS) was mixed carefully by flicking the microcentrifuge tube with 3 µL buffer D2 (1 M DTT and buffer DLB, denaturation buffer). Cell lysis was incubated for 10 minutes at 65°C, and 3 µL stop solution was added. To the total denatured gDNA (10 µL), 40 µL of master mix (9 µL H2O, 29 µL REPLI-g sc reaction buffer Solution and 2 µL REPLI-g sc DNA polymerase) was added to the reaction. This solution (50 µL) was gently mixed and incubated at 30°C for 8 hours and heat inactivated at 65°C for 3 minutes.

The gDNA concentration and quality was measured by Qubit 4.0 Fluorometer (Thermo Fischer Scientific, USA), and the Integrity of the DNA sample was evaluated by agarose gel electrophoresis (Agarose concentration 1%, voltage 120V, 45 minutes). After quality control, the library was prepared using 0.2 µg gDNA as template according to the Watchmaker DNA Library Prep Kit with Fragmentation (7K0019-096). DNA concentration of the library was measured in Qubit 4.0 and fragment size was assessed using the Fragment analyzer system. Q-PCR was performed using ABI Quant Studio 12K Flex to accurately quantify the effective concentration of the library (the effective concentration of the library > 10nM). The cluster generation and sequencing were performed on Novaseq 6000 S4 platform at Annoroad Gene Technology Company (Beijing, China), using NovaSeq 6000 S4 Reagent kit V1.5.

Candidate eccDNA validation by PCR
eccDNAs of interest were verified using reverse PCR and Sanger sequencing. The reaction system is 25 µL, consisting of 1 µL DNA product, 2 µL forward primer (5 µM), 2 µL reverse primer (5 µM), 12.5 µL 2×Rapid Taq main mixture (Vazyme), and the corresponding amount of ddH2O. The hot cycle PCR procedure was 95°C, 2 minutes, 35 cycles of (95°C 15 seconds, 55°C 15 seconds, 72°C 5 seconds), 72°C for 5 minutes and 4°C hold.

eccDNA synthesis via Ligation-Assisted Minicircle Amplification (LAMA)
The synthetic eccDNA was produced using the LAMA method52. The random 700 bp DNA sequence for control was generated with "random DNA sequence generator" (http://www.faculty.ucr.edu/~mmaduro/random.htm) web tools with a 50% guanine–cytosine content. The half-complemental linear DNA fragments of each eccDNA and its corresponding PCR primers were synthesized in GENEWIZ. Sequences of linear DNA fragments and PCR primers were listed in the Supplementary Data 12. LAMA reaction involves mixing equal amounts of linear A and B amplification products with Taq DNA ligase (NEB) and buffer. The thermal cycle system used is 5 minutes at 95°C, (95°C 20 seconds, 4°C 1 minute, 55°C 20 minutes) with 10 cycles. The residual linear DNA was removed by exonuclease digestion of V (NEB) and the LAMA reaction products were purified by magnetic beads (Vazyme). Then the synthetic eccDNA was digested by certain restriction enzymes using the linear fragments as controls to verify its circular structure. EccDNA with only one linear band after restriction enzymes digestion was selected for subsequent experiments.

Transcription inhibition assay
For inhibition of both minor and major ZGA, embryos were exposed to 0.1 mM α-amanitin (MedChemExpress, HY-19610) from the zygote stage at 6 hours post in vitro fertilization (IVF) until the 2-Cell stage at 35 hours post-IVF for Smart-seq2 (n = 15 for each group) and WGS (n = 100 for each group) analysis. The same volume of solvent (H2O) was used as a control treatment for the embryos. We used microscopy to verify the effect of α-amanitin on embryonic development at 40 hours post-IVF, when the control embryos had reached the 4-Cell stage.

scRNA-seq library preparation and sequencing
The RNA-seq libraries were constructed from both control and inhibition embryos following the Smart-seq2 protocol68. Cells were lysed using a hypotonic lysis buffer (Amresco, M334), and polyadenylated mRNAs were captured with PolyT primers. After incubating the lysate for approximately 5 minutes at 72 °C, reverse transcription reactions were carried out using the Smart-seq2 approach. The cDNA underwent pre-amplification and purification with AMPure XP beads, after which they were fragmented using a Covaris system and prepared for sequencing with the Illumina TruSeq library kit. Sequencing was performed on NovaSeq™ X Plus sequencers following the manufacturer’s guidelines. Each sample was analyzed in two separate biological replicates.

eccDNA microinjection and RT-qPCR in NIH 3T3 cells
Mouse embryonic fibroblasts NIH 3T3, purchased from the Central Laboratory of Peking University Third Hospital, were cultured in DMEM medium (HyClone) supplemented with 10% fetal bovine serum (FBS) (Biological Industries (BI)) and 1% penicillin/streptomycin (Invitrogen). Cells were cultured in an incubator at 37°C with 5% CO2. Lipomaster 2000 transfection reagent (Vazyme) was used. For each transfection, 500 ng eccDNA and 1.5 μL Lipofectamine 2000 was diluted with Opti-MEM (Gibco) to a total volume of 25 μL, then the diluted DNA was added to the diluted lipofectamine in a ratio of 1:1. After incubation for 10 minutes, the transfer solution was uniformly injected into the 60000 adherent cells in 24-well plates. Each group was performed with three technical replicates. Total RNA was extracted with FastPure Cell/Tissue Total RNA Isolation Kit V2 (Vazyme) 48 hours after transfection, and was reverse transcribed into cDNA according to the instructions of HiScript III 1st strand cDNA Synthesis Kit (+ gDNA wiper) (Vazyme). qPCR was carried out using ChamQ Universal SYBR qPCR Master Mix (Vazyme). All primers used in the qPCR experiments were listed in Supplementary Data 12.

eccDNA transfection and RT-qPCR in embryo samples
Zygotes were pre-incubated in M2 medium before microinjection. Using an Eppendorf Transferman NK2 micromanipulator, approximately 5 pL of eccDNA solution (300 ng/µL) was microinjected into the cytoplasm of each zygote. Post-injection, embryos were cultured in KSOM medium at 37°C under 5% CO2. cDNA was synthesized from embryonic microsamples following established protocols69. Briefly, groups of five or ten embryos were collected, washed three times in PBS containing 0.2% BSA, and lysed in 2 μL of lysis buffer (0.2% Triton X-100 supplemented with RNase inhibitor). Random primers and dNTP mix were added to the lysate, followed by hybridization in a PCR thermocycler. Reverse transcription was performed using PrimeScript II Reverse Transcriptase (TaKaRa) according to the manufacturer’s instructions. Quantitative PCR analysis was carried out with Power SYBR Green PCR Master Mix (Applied Biosystems) on an Applied Biosystems Real-Time PCR system.

Immunofluorescence staining and confocal microscopy
For immunofluorescence, embryos were fixed in 4% paraformaldehyde (in PBS) for 30 minutes at room temperature (RT), permeabilized with 0.3% Triton X-100 (in PBS) for 15 minutes, and blocked with 1% BSA (in PBS) for 1 hour. Samples were then incubated overnight at 4°C with primary antibodies against phospho-H2A.X (γH2A.X, #9718S; Cell Signaling Technology; 1:400 dilution in blocking solution). After three washes in PBS, zygotes were incubated with fluorophore-conjugated secondary antibodies containing Hoechst 33342 for 30 minutes at RT. Finally, samples were washed, mounted on glass slides using SlowFade Gold Antifade Mountant (Life Technologies), and imaged on a Zeiss LSM980 confocal microscope. Fluorescence intensity was quantified using ImageJ software.

Embryo culture with ML323 inhibitor
ML323 (SML1177, Sigma-Aldrich) is a potent and selective inhibitor of the USP1–UAF1 deubiquitinase complex70, 71. Previous studies in somatic cells demonstrated that treatment with a final concentration of 30 μM for 3–6 hours effectively inhibits USP1–UAF1 activity72, 73. For embryo culture, ML323 was first dissolved in dimethyl sulfoxide (DMSO) to prepare a 10 mM stock solution. Prior to use, the stock was diluted with KSOM embryo culture medium to working concentrations of 10 μM, 50 μM, and 100 μM. These working solutions were equilibrated overnight for more than 6 hours in an incubator maintained at 37°C with 5% CO2.

Female C57BL/6J mice were intraperitoneally injected with 5 IU pregnant mare serum gonadotropin (PMSG). After 48 hours, 5 IU human chorionic gonadotropin (hCG) was administered, and females were subsequently paired with adult males for mating. Approximately 20 hours post-coitum, fertilized zygotes were collected, thoroughly washed, and transferred into KSOM medium containing different concentrations of ML323 for continued culture. DMSO was used as the control. Embryo development rates and immunofluorescence staining were recorded and compared across the treatment groups.

The effect of ML323 concentration was first tested using in vivo fertilized embryos, and consistent results were confirmed with IVF embryos. Due to the limited yield from in vivo fertilization, we proceeded with IVF-derived embryos for subsequent WGS. For WGS, embryos generated by IVF at the 2-Cell stage were used, with 100 embryos analyzed per group.

Public low-input ATAC-seq data of embryos
All ATAC-seq data used in this study were obtained from publicly available datasets reported by Wu et al.43, 74, covering human (six consecutive stages) and mouse (five consecutive stages). These stages comprise the zygote (only in human), 2-Cell, 4-Cell, 8-Cell, and blastocyst-represented by the inner cell mass (ICM)-along with ICM-derived embryonic stem cells (ESCs). These publicly available datasets were used for downstream analyses without additional experimental manipulation.

Identification and filtering of eccDNA
Reference genomes for human and mouse were from Ensembl GRCh38 Release 107 and Ensembl GRCm38 Release 99. To ensure the quality of eccDNA identified, we used the masked genomic DNA (interspersed repeats and low complexity regions are masked by replacing repeats with 'N's) and then also masked the blacklist (https://github.com/Boyle-Lab/Blacklist/). BWA-MEM (v0.7.18-r1243-dirty)75, with the default setting, was used to map paired-ended reads to the hg38 and mm10 genome build. Using samblaster (v0.1.26)76 to collect split reads and discordant reads. The complete process to identify eccDNA was accessible through the GitHub https://github.com/pk7zuva/Circle_finder. To improve detection accuracy, obtained eccDNAs were filtered to remove those mapped to unplaced genomic scaffolds (e.g., chrGL*, chrJH*), mitochondrial genes, and regions longer than 50 Mb. Downstream analyses focused primarily on relatively short eccDNAs by applying strict filtering criteria: fragment sizes were limited to 100-1,000,000 bp, and each eccDNA was required to have at least 1 split read and a combined total of ≥4 split plus discordant reads (Supplementary Figure 3C).

Quantification of eccDNA
We conducted a quantitative analysis of eccDNA by devising a counting approach. Specifically, we classified reads into split reads, discordant reads, and concordant reads based on the above pipeline. 1) For split reads, we used the BEDTools v2.30.077 suite pairToBed (do pairToBed -type both) to output reads that are aligned to both ends of eccDNA and were on the same direction chain of DNA. Then we filtered these reads to obtain preliminary eccDNA reads and ultimately calculated the number of split reads supporting each eccDNA. 2) For concordant reads, we employed “bedtools multicov” to calculate the number of concordant reads that overlap with eccDNA, and subsequently tallied the number of concordant reads that corroborate each eccDNA. 3) For discordant reads, both reads must be located entirely within eccDNA regions and have opposite orientations, with one read on the positive strand and the other on the negative strand. Following this criterion, we then counted the number of discordant reads that support each eccDNA.

Based on these three distinct types of sequencing reads, we defined the following variables: si, representing the number of split reads supporting eccDNA, i, in each sample; di, indicating the number of discordant reads supporting eccDNA, i, in each sample; and ci, representing the number of concordant reads supporting eccDNA, i, in each sample. We also introduced ri, which is defined as:
ri = (si + di) / (si + di + ci) (1)
ri represented the proportion of split and discordant reads among all three types of reads. Next, we used ri to calculate the abundance of eccDNA, i:
Ei = ri × ci + si + di (2)
In this formula, we multiplied ri with ci to get an estimated contribution from concordant reads towards eccDNA support, and then added the actual numbers of split and discordant reads. Ultimately, Ei was used to calculate Transcripts per Million (TPM) values, which then represents the abundance of eccDNA. To assess the effect of eccDNA length on abundance estimation, we performed correlation analysis between eccDNA length and its abundance across all stages. The analysis revealed no significant or consistent correlation (Supplementary Figure 4I), indicating that eccDNA size does not systematically bias quantification. This supports the robustness of our abundance estimation method.

Gene expression data processing
Public RNA-seq data were obtained from the Gene Expression Omnibus (GEO) GSE6658243 and GSE4418349. For all datasets, raw reads were trimmed using fastp (version 0.23.2) to remove the primer sequences as well as low-quality bases with the parameters -q 20 -u 30, followed by mapped to ENSEMBL reference genome (corresponding to hg38 or mm10) by HISAT2 (version 2.2.1) software 78 with the default settings. The reads mapped to each gene were counted using featureCounts79 (Version 2.0.1) under the parameters -p -a gtf -g gene_id, basing on the gene annotation files from Ensembl. For downstream analysis, we standardized the read counts by calculating TPM, and then normalized counts were log10-transformed following the addition of a pseudocount of 1.

Analysis of public chromatin datasets
The published H3K4me3 ChIP-seq data for human 4-Cell embryos, 8-Cell embryos and ICM, along with the H3K27ac ChIP-seq data of human 8-Cell embryos and ICM were collected from GSE12471846. The H3K4me3 ChIP-seq data for mouse early embryos per stage was pulled from GSE7143442. The H3K27ac ChIP-seq data for mouse 2-Cell embryos, 8-Cell embryos, and ESC were curated from GSE66390 and GSE72784, respectively43, 44. The Pol II datasets of mouse early development were from GSE13545745. All reads were trimmed using fastp (version 0.23.2) with parameters -z 4 -q 20 -u 30. Following trimming, reads were aligned to the GRCh38 or GRCm38 reference genome using Bowtie 2 (version 2.3.5.1)80, then sorted and indexed using SAMtools (version 1.15) with default parameters81. For downstream analysis, “bamCoverage” function in DeepTools (version 2.5.7)82 was utilized to generate normalized RPKM.bw files. Subsequently, DeepTools was employed for heatmap visualization using the 'computeMatrix' and 'plotProfile' functions.

Overlaps of eccDNAs from human preimplantation development with de novo mutations
We used “bedtools shuffle” to generate a random set of regions that match the lengths of eccDNA for 1000 times, which used as a control. Then, “bedtools intersect” was used to calculate the number of overlaps between these regions and the target.

Genomic feature acquisition
To determine chromosomal features per Mb relative to the recorded number of eccDNA per Mb, we extracted genomic features, such as chromosome length and the number of genes, from GRCh38 (Ensembl release 107) or GRCm38 (Ensembl release 99). Additionally, the number of Alu elements on each chromosome was obtained from Homer (http://homer.ucsd.edu/homer/).

Definition of gained, lost, stable, stage-specific, and consistent eccDNAs
To explore the dynamics of eccDNA, eccDNA presents at a specific stage but absent in the preceding stage were designated as “gain” eccDNAs for that stage. Conversely, eccDNAs detected at a given stage but absent in the subsequent stage were categorized as “loss” eccDNAs for that stage. Additionally, eccDNAs present at a stage and also observed in the preceding stage were classified as “stable” eccDNAs for that stage. All comparisons were performed using a stringent criterion of 100% overlap between eccDNA genomic coordinates. “Stage-specific eccDNAs” refer to those detected only at the current stage, with no coordinate overlap with eccDNAs from other stages. “Consistent eccDNAs” are defined as those present across all developmental stages, exhibiting complete coordinate overlap.

Genomic distribution and gene annotation of eccDNA
To delineate the genomic localization of eccDNA, eccDNA was designated according to the priority order (promoter > 5' UTR > 3' UTR > exon > intron > downstream > intergenic) facilitated by ChIPseeker R package (v1.34.1)83 in cases where a single eccDNA encompassed multiple genomic features. The identification of eccDNA-associated genes was carried out by determining the nearest genes to eccDNA using the ChIPseeker package.

Gene ontology (GO) enrichment analysis
GO enrichment analysis was conducted utilizing the 'enrichGO' functions of the ClusterProfiler R package (v4.6.2)84. The P values were adjusted using the Benjamini-Hochberg correction to account for multiple comparisons, focusing on statistically significant enrichment.

Characterization of the eccDNA junction sites and motif-enrichment analysis
BEDTools v2.30.0 was employed to extract DNA sequences, comprising 20 base pairs upstream and downstream of every eccDNA junction sites. Motif enrichment analyses were conducted using HOMER (version 4.11)85 findMotifsGenome.pl algorithm with the parameters ‘-size given and -mask’, leading to known enrichment results and de novo enrichment results, the former were selected and utilized.

Fuzzy C-means Clustering
eccDNA obtained from six consecutive embryonic stages were clustered into distinct groups using the Mfuzz R package (v2.58.0), employing the fuzzy c-means algorithm86, allowing for a comprehensive understanding of temporal expression trends.

Correlation between eccDNA and RNA expression
In the case of dynamic changes of eccDNA and RNA expression, the input consisted of log10-transformed TPM values derived from each stage. A correlation analysis was performed between eccDNA and RNA expression levels in shared genes, utilizing Spearman’s correlation coefficient with cutoff of P value < 0.01 and a correlation coefficient of 0.7 for positive (or negative -0.7) were employed.

Structural variant (SAV) calling in preimplantation embryo MDA samples
SVs in genomes from various developmental stages were identified using the lumpyexpress function of LUMPY (v0.2.13)65 with default parameter settings. We then quantified the major SV types, including inversions, translocations, deletions, and duplications. To assess their association with eccDNAs, overlap analysis was performed using a 90% reciprocal overlap threshold as the criterion for counting shared regions.

Inference of copy number variation
We employed Ginkgo66 to analyze genome-wide copy number values (CNVs) on MDA-based WGS datasets using 100-kb genomic bins. Subsequently, we performed statistical analysis on the CNVs of each region across different developmental stages.

AmpliconArchitect (AA) analysis
AmpliconSuite (version 1.4.0)67 was used to process MDA-based WGS datasets, including the reconstruction of focal amplifications and potential circular amplicons. BAM files were first processed with PrepareAA, and seed intervals were defined based on regions of high copy number, as inferred from CNV profiles. AA was then run with default parameters to reconstruct the architecture of amplified regions and to determine the presence of cyclic structures.

Statistics & Reproducibility
No statistical method was used to predetermine sample size. Sample sizes were determined based on embryo availability and previous published studies. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. Statistical analyses were performed using R (version 4.2.1). Principal component analysis was conducted using the prcomp function in R. Data visualization was performed using the ggplot2 package (version 3.4.3).

Data Availability
Raw sequencing datasets of MDA and scRNA-seq reported in this paper have been deposited in the Genome Sequence Archive (GSA) in the National Genomics Data Center, China National Center for Bioinformation, Chinese Academy of Sciences under accession number CRA019281 [https://ngdc.cncb.ac.cn/gsa/browse/CRA019281]. Publicly available ATAC-seq and other datasets used in this study were obtained from the Gene Expression Omnibus (GEO) under the following accession codes: GSE66582 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66582], GSE44183 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44183], GSE124718 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124718], GSE71434 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71434], GSE66390 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66390], GSE72784 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72784], GSE135457 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135457]. Source data are provided with this paper.

Code availability
All scripts used for eccDNA quantification in this study are publicly available at GitHub: https://github.com/duck-rong/eccDNA_quantification_scripts. No software was developed beyond these scripts; all analyses were performed using these scripts together with publicly available tools as described in the Methods section.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Extrachromosomal circular DNA (eccDNA) has been continuously gaining broad interests due to development of tools and methods to better understanding their roles in health and diseases. In the current study, Wei and colleagues applied a comprehensive sequencing approach to study the landscape of eccDNA in preimplantation mouse and human embryos. The study was primarily based on mouse embryos due to ethical considerations but reflected on human ones with public datasets. Using the multiple displacement amplification (MDA)-based whole genome sequencing and a modified circle-finder pipeline, the study overcome the limitation of limited genomic materials from preimplantation embryos and identified nearly 400,000 eccDNA from early mouse embryos. The study identified larger eccDNA in the 2-cell stage of mouse embryos, overlapping with the mouse zygotic genome activation stage (ZGA). While most eccDNA were stage-specific (only detected at one stage), the study identified a surprisingly large number (2,285) of so-called consistent eccDNAs across all stages. The finding what I found to be most interesting and important for understanding the roles of eccDNA in early embryo development are related to their overlap with ZGA genes, enrichment of TF motifs at the eccDNA junctions, and their cis regulating role in own gene expression. Although the study has performed some functional validation of the selected eccDNA, e.g., the Mycn circle, carefully addressing any confounding effect is still required to further support this conclusion. See detail comments below.\nMajor comments\n1. Regarding the methods for eccDNA detection\nI understand that the authors applied the MDA-based to overcome the issue of small amount of genomic DNA obtained from the embryos. Compared to Circle-seq, which enriches the circular form of DNA from cells/tissues, it seems that the length of eccDNA detected by MDA is larger (see. Figure 1C). Have the authors assessed and compared the false discovery rate of the MDA method to the Circle-seq? This is not to judge which method is better, as Circle-seq also has many limitations e.g. preferring the small circles. In principle, DMA could not distinguish circular DNA from other forms of genomic alterations e.g. inversions, translocations.\n2. Regarding the quantification of eccDNAs.\nThe study introduces a sound quantification method for eccDNA, with an attempt to measure the eccDNA abundance. The current method introduces factor r to adjust for the length of concordant reads. Since the equation is now Ei = r*Ci + Si + Di, would the discordant reads increase with the eccDNA size? Which means that large eccDNA will be quantified as “high copy number”? Please check if there is a correlation between the size and quantification eccDNA to exclude the potential effect.\n3. Regarding the origin of eccDNAs detected in the embryos.\nThe data regarding the eccDNA counts presented in Figure 1B provides a very interesting pattern that the authors might want to evaluate further. That is the origin of these large amount of embryonic eccDNAs.\nInstead of originating from the fertilized maternal and paternal genomes, these increasingly large amounts of eccDNAs might come from the copy of “discarded” maternal polar body. This will explain why there are significantly higher number of eccDNA found in the 1C, 2C, and 4C stages.\nRationally, it is quite logic to postulate that if the eccDNA are derived from the fertilized genomes, they will create corresponding deletion scars, leading to permanent genomic deletions in the subsequent cells. Depending on the heterozygosity of the sperm and oocytes, it might be possible and interesting to determine the eccDNAs derived from paternal and maternal (polar body) genomes. Alternatively, crossing oocyte and sperm from two strains (with diverse SNP) should be able to address.\nThe second polar body contributes embryo development. However, most studies have been focused on the carry-on transcripts and proteins. Since the genome of second polar body will go into the apoptosis processed, which is directed linked to eccDNA formation. Addressing this link will significantly increase the novelty and findings of this study.\n4. Regarding the statistics of overlapping eccDNA with ZGA genes.\nA few analyses in the current study will benefit from more statistic analyze to acutely reflect the significance.\nFor examples, there are 1229 ZGA genes overlap with the eccDNA-associated genes gained in the 2-cell stages. It is important to test whether this is overrepresented than by chance. The author can randomly subset same number of genes from the mouse genome (and from the non-ZGA genes), and carry out similar overlapping analysis, following by testing the enrichment with Fisher exact test. This is important to test whether the eccDNA found at the 2-Cell stage are more enriched to ZGA genes than other genes.\nSimilarly, same testing should be applied to Figure 3G, Figure 4D, Figure 5D. In Figure 5D, there are 78 genes overlap between eccDNA+ and ZGA genes. If you randomly subset the same number of genes as eccDNA+ genes and see how many of these will overlap with ZGA genes by chance.\nOther minor comments\n• Abstract. It is still quite odd to call eccDNA as a novel epigenetic regulatory element. Small eccDNA comprising cis regulatory element could regulate distance gene expression, but not via the classical epigenetic path.\n• Figure 1. A, why the 8C image is smaller than others. B. what are the number of eccDNAs same between rep 1 and rep2? C. provide the value of bp for the eccDNA peaks; G. value provided for the length of eccDNA peaks in figure.\n• Figure 2. B. texts (length of direct repeats) should be placed at the right position. The pie chart values are fractions. D, are the TF motifs only detected for 2C eccDNAs?\n• Figure 3. Re-order that figures so that sub figures will appear later. D and G were above C and E, making it hard to follow.\n• Related to last part of eccDNA and human developmental disorders, the current data and results are not sufficient to draw this conclusion. This is because the eccDNAs derived from ATAC-seq are already bias to gene regions. Furthermore, since most eccDNAs are exhibiting high diversity and different from stages to stages, the interpretation will change depending on the eccDNAs from what stages.", "reviewer_2": "Ling Wei et al. presents an integrative profile of eccDNA landscape, formation mechanism and potential function in mammalian preimplantation embryos, advancing the understanding of eccDNA involved in early mammalian embryogenesis. However, additional analyses may help further improve the manuscript before considering it for publication in Nature Communications. Specific comments are as follows:\nMajor revisions:\n1. The authors used Circle-finder to detect eccDNAs from MDA data, identifying tens of thousands of eccDNAs per sample—far exceeding the numbers reported in existing studies. How can the authors prove that these detected eccDNAs are not ordinary structural variants (SVs)? What is the performance of Circle-finder’s assembly mode? The authors should employ alternative methods, such as Circle-seq-based approaches (PMID: 37142849, PMID: 38409079), to validate eccDNA detection.\n2. In line 183, the authors claim that AmpliconArchitect (AA) detected tens of ecDNAs is also quite surprising, especially given the authors’ statement that no significant CNV features were observed in the samples. The authors need to clarify the correct workflow in the Methods section and visualize ecDNA examples using the standard presentation format commonly used in AA. In addition, in Figure S1C, it can be seen that the ecDNA identified by AA is the most in the 4C stage. Usually, the ecDNAs identified by AA is the large fragment rich in oncogenes. Does this indicate that the 4C stage contains more functional ecDNAs?\n3. Many findings in the manuscript lack novelty, such as the chromosomal distribution of eccDNAs and ALU features, which already have been widely reported (e.g. PMID: 37142849). These should not occupy space in the main figures. Besides, the method used in other literature for calculating the frequency relative to the chromosome of eccDNAs: number of eccDNAs for all autosomes normalized for the chromosome size, not only ecDNA counts.\n4. The manuscript lacks a clear and detailed definition of terms such as “overlapping” and “consistent” eccDNAs. For instance, in Supplementary Figure S1A, it remains unclear how the eccDNAs were selected to represent the overlap between the 1C and 2C stages in the pie chart. Is this determination based on the percentage of sequence overlap between eccDNAs from the two stages? Furthermore, in the Methods section (lines 706–712), the definitions of “gain,” “loss,” and “maintained” eccDNAs are vague. Specifically, how is it determined that an eccDNA is the same between different stages—does it require an exact match of genomic coordinates, partial sequence similarity, or another criterion? The authors should clarify the criteria and thresholds used for defining and quantifying shared or stage-specific eccDNAs.\n5. The transcriptional activity of eccDNA-originating genomic regions has been previously described (e.g., PMID: 26051933). While the authors propose that eccDNA formation is linked to transcription–replication conflicts—an intriguing and potentially novel hypothesis—this idea is only discussed based on existing literature, without new supporting data. It is strongly recommended that the authors provide experimental or computational evidence to substantiate this claim. Incorporating approaches similar to those used in PMID: 26051933 could enhance the manuscript's rigor.\n6. In line 201, the authors assert that certain eccDNAs can persist stably across developmental stages. This is a striking claim, as eccDNAs are typically considered non-heritable. The authors should clarify the definition of “stable eccDNAs” and investigate their sequence features—e.g., are they associated with telomeres, centromeric satellites, simple repeats, or transposable elements? Additionally, what biological roles (if any) might these eccDNAs play during embryonic development?\n7. In Figure 2D, transcription factor (TF) binding motifs are enriched at eccDNA junction sites rather than in central regions. This observation appears counterintuitive, given that TF binding is generally thought to protect DNA from breakage. A plausible mechanistic explanation is needed to support this result.\n8. Similarly, why are active histone modifications (e.g., H3K27ac, H3K4me3) enriched specifically at eccDNA junctions, rather than across the entire eccDNA sequence? The authors may also consider analyzing base composition (e.g., AT- or CG-content) near junction sites across developmental stages, analogous to studies in cancer systems.\n9. In lines 300–303, the authors report that Gene Ontology (GO) analysis of genes downregulated after α-amanitin treatment identified pathways critical for ZGA, including RNA metabolism and nucleic acid metabolism. However, these pathways may be enriched simply because eccDNA-associated genes are largely ZGA-related. Clarification is needed: was the GO analysis in Fig. 3H performed using all downregulated genes as the background set? If so, the result may not reflect specific enrichment among eccDNA-associated genes but rather the overall gene set affected by transcriptional inhibition.\n10. A similar concern applies to Fig. 5E. If ZGA gene expression naturally increases at the 2-cell stage, then the observed increase in “overlapping genes” is unsurprising, as they are a subset of ZGA genes. Therefore, this observation alone does not support the conclusion that “eccDNA may play a critical role in transcriptional activation during the ZGA process.”\n11. In lines 355–357, the paragraph begins with an example of eccDNA acting as a mobile enhancer in tumors. However, the subsequent discussion focuses on eccDNA genes rather than enhancer-like functions. This creates a logical inconsistency. It is recommended to restructure this section to align the examples more clearly with the points being discussed.\n12. In Figure 5A, the manuscript compares RNA expression levels between eccDNA-associated genes and other genes. However, the number of genes in each group is not indicated. It is unclear whether the “other genes” category includes a large number of non-expressed genes, potentially skewing the comparison. To improve clarity, a violin plot should be used to visualize the distribution of gene expression more effectively.\n13. In Figures 4D and 5D, the authors state that eccDNAs present at the 2-cell stage overlap significantly with ZGA genes. However, these conclusions are not statistically supported. For instance, in Fig. 5D, only 78 of 4,058 genes overlap. Statistical significance tests (e.g., Fisher’s exact test or hypergeometric tests) are needed to validate these claims.\n14. In Figure 4A, PCA of eccDNA data shows that PC1 is largely driven by BL-stage samples, which are not represented in the RNA-seq data. As a result, the correlation between eccDNA and gene expression may be misleading. The PCA should be repeated with the BL samples excluded to ensure consistency and comparability across data types.\n15. The manuscript contains a critical causal ambiguity. The authors propose that eccDNA promotes the ZGA process by enhancing transcription of associated genes. However, it is equally plausible—and perhaps more likely—that transcriptional activation leads to increased eccDNA formation near expressed genes. Given that eccDNAs are annotated based on proximity to nearby genes, the observed correlation may reflect this transcription-driven mechanism. To establish causality, the authors must show that eccDNAs harbor enhancer elements that can modulate gene expression, ideally through functional assays (e.g., eccDNA transfection into mouse embryonic fibroblasts followed by assessment of ZGA gene activation).\nMinor revisions:\n1. In Figure S2B, why can such a significant p value be produced at 20 points (P = 5.3e 13, R = 0.099)? Each chromosome needs to be annotated in the dot plot. Besides, the conclusion of \"slightly positive correlation or significant and consistent positive association\" in the main text is too far-fetched.\n2. In Figure S4A, why does the number of eccDNA identified using the public ATAC-seq datasets reaches its peak during the ICM period not match the conclusion that the 2C stage had the largest number of eccDNA as studied in the previous text (Figure 1B)?\n3. In Methods (line 633-634), ‘Obtained eccDNA were filtered out for gene fragments, mitochondrial genes and over 50M long’. Filtering mitochondrial genes and over 50Mb of eccDNA can be understood. Why remove eccDNA containing gene fragments? Moreover, the author has compared the positive correlation between the abundance of eccDNA and the gene frequency of ZGA genes containing eccDNA on multiple occasions in the article.\n4. Does heatmap in Fig 5B means z-score of expression?\n5. In Figure 5G, H and I, the author artificially synthesized eccDNA carrying enhancer and transfected it into mouse embryonic fibroblasts to observe transcriptome changes. How many biological replicates were done? It is recommended to draw the dots in the boxplot. In addition, whether the mouse embryonic fibroblasts transfected by the author are definite ecDNA-negative cells is suggested to be stated in the main text. Refer to PMID: 33836152.", "reviewer_3": "Wei and colleagues set out to detect, measure and characterize extrachromosomal circular DNA (eccDNA) in mammalian preimplantation embryos using mouse embryos generated by IVF and embryo culture. They report the occurrence of widespread eccDNA in these stages, and used a series of omics data to interrogate correlations between eccDNA sequences and genome regulatory activities happening in early embryos. Overall eccDNA was correlated with transcription-replication conflicts. They also provide an experiment somewhat detached from the rest of the manuscript showing that eccDNA carrying a gene, Mycn, is associated with a higher expression of this gene when transfected into mouse embryonic fibroblasts.\nThe topic of eccDNA is a very interesting and potentially relevant one for our understanding of genome stability and regulation. However, I think the findings of the authors need to be better controlled risking that the vast majority of the eccDNA detected in pre implantation mouse embryos are artifactual. Without such control, or the explicit argumentation of this possibility in the text (and abstract) so that readers and future efforts are not misled, I cannot be supportive of the publication of the manuscript in its current format/content.\n1. Abstract, line 33: what is the evidence to call eccDNA a “novel epigenetic regulatory element”? Under which definition of epigenetic such statement is true?\n2. Abstract lines 35-37: “its function remains elusive in early embryonic development which also involves rapid cell proliferation” is a factually incorrect statement. Some of the slowest cell division speeds are found in mammalian pre-implantation embryos, and even a slow replication timing has been implicated in ZGA in 2C mouse embryos. Rapid proliferation, if any, happens after implantation, encompassing stages the authors have not investigated in this work.\n3. I have one very strong criticism towards the findings and that is that the authors cannot rule out that the eccDNA found and measured emanates from the media in which the embryos were collected and IVF was performed, remnants of cumulus cells surrounding the collected MII oocytes, or even the polar body that stays in the fertilized embryos for a few days. Sperm, in particular, is an abundant source of eccDNA, and in my view it is likely that the transfer of oocytes to fresh media with capillary devices is unlikely to be enough to get rid of surrounding contaminated media/particles – not to mention whatever gets stuck to zona pelucida and whatever is contributed by the polar body.\n- What measures were taken to control for contamination eccDNA coming from the sources mentioned above?\nA few extra point that contribute to the rationale challenging the measurements and characterization of eccDNA in mouse pre implantation embryos:\n- A zygote or even a 2C embryo have a very limited number of alleles for each loci, namely 2 and 4. If such extensive eccDNA is formed, unless the assumption is that eccDNA will repair itself back into its source loci, what evidence is there that there is extensive loss of heterozygosity (LOH) in pre-implantation embryos? So early on development, such an event can potentially spread to the cell of the whole body on individuals, and yet we don’t have any evidence of that.\n- One potential way of controlling for this would be to use hybrid mouse zygotes (e.g. C57Bl/6 with CAST/EiJ) for which the SNP density would allow researchers to QC their data on eccDNA source and answer what happens to source loci in later stages of pre-implantation development and further.", "reviewer_4": "In their manuscript Wei and colleagues provided for the first time, in the comprehensive stage-by-stage way, the quantitative and qualitative descriptions of Extrachromosomal circular DNA (eccDNA) in mouse preimplantation embryos. The authors suggested the link between eccDNA, a zygotic genome activation (ZGA) and other important processes that occur in mouse embryos. They proposed that eccDNA is involved regulation of gene expression during early development. In addition, the authors analysed published public ATAC-seq datasets of mouse and human preimplantation development and concluded that widespread presence of eccDNA is a common feature of mammalian embryonic development.\nWhile providing potentially interesting information for the researchers working in the field of eccDNA, the manuscript falls short to be suitable for the wider scientific audience. The current version of the manuscript is very descriptive and has quite a narrow focus on the features of eccDNA present during early mouse development. While, it is potentially an important work, the significance of these observations in a preimplantation development context is currently unclear as most of the suggested roles and associations of eccDNA with the important developmental processes are not supported by strong functional data.\nHere I am listing potential extensions of the current work to increase the impact of the manuscript:\n1. The link between eccDNA and ZGA is interesting and definitely worth investigating. However, the current manuscript falls short of providing a direct functional proof that eccDNA are necessary for ZGA initiation (or ZGA progression).\n2. The observation described in line 222-225 “This observation highlights the potential importance of consistent eccDNA, particularly their location in promoter regions, which may indicate a role in regulating gene expression across all stages of preimplantation embryo development.” is an interesting and potentially important point and the functional follow up could vastly improve the impact of the manuscript.\n3. The link between eccDNA and multiple developmental genes, such as Rfpl4b and Zfp352 in the 2-Cell stage, Tspan1, Xist, Sall1 andTbx20, Pax6 and Gjb3 in the 8-Cell stage, and Zfp51 in the blastocyst stage are strongly suggested, but never functionally confirmed.\n4. In the line 380 to 382 the authors wrote: “These observations suggested that eccDNA+ genes may be crucial for early events in preimplantation embryonic development, including the initiation of the first DNA replication, cell fate determination, and ZGA process.” However, the authors did not present any functional data to support the role of eccDNA in any of these processes. While the authors synthesized eccDNA carrying the enhancer of Mycn gene and subsequently transfected it into mouse embryonic fibroblasts and subsequently observed the expression level of Mycn was significantly higher in eccDNA transfected than that of the control group, they did not confirm if the same will be true in preimplantation embryos. Similar experiments should be performed on early embryos and the impact on the developmental processes linked by the authors to eccDNA presence should be clearly demonstrated.\nImportant concerns about embryo work\nI cannot find the number of embryos that were used per each biological duplicate for initial eccDNA analysis (Fig 1) per each stage. Moreover, it is not clear if all the embryos were collected from the same litter, or different litters were investigated. Similarly, it is not clear how many embryos per one biological duplicate was analysed in experiments with α-amanitin. The data from fig S3B are not sufficiently explained to be a guide here.\nAnother important concern is that all the embryos used in this study seems to be IVF fertilised and cultured embryos. As it is well known that IVF culture of mammalian embryos may alter some of the processes during development, it would be important to validate at least some of the observation using freshly flushed embryos (not only the cultured ones) to account for the culture artifacts.\nMinor points\n1. Did the authors observe eccDNA associated with Klf5 only at the 2cell stage? What about other stages?\n2. The title for fig 4 doesn’t provide any useful information. What does “early embryonic development processes” means? ZGA? Cleavages control? The whole preimplantation development including lineage specification? The authors seem to use this term loosely and in different context thought the manuscript so at the end it is impossible to find out what they had in mind.\n3. What is the size of scale bar in fig 1A?\n4. In the line 400 the authors wrote: “We found that the quantity of eccDNA reaches its peak during the ICM period”. It is not clear what “ICM period” means. The ICM stands for inner cell mass, but this is NOT a “period” or “stage”. It is a group of cells within the blastocyst. This whole sentence is very confusing and needs re-wording. The same is true for the line 431-32.\n5. Line 526 – term “zygotic cells” should be change to “fertilized zygotes”"}, "author_response": "RE: We sincerely thank the reviewer for raising this important question, and we have implemented multiple controls, analyses, and validations to address this concern as detailed below:\n(1) Justification and rationale for using MDA\n①Due to the extremely limited amount of genomic DNA available from preimplantation embryos, it was not feasible to perform Circle-seq. We acknowledge this limitation and have explicitly stated it in the revised manuscript (Lines 622-627).\n②Previous studies have demonstrated the feasibility of applying MDA for eccDNA detection under low-input conditions. For example, Zhang et al. successfully amplified 1 cell-free eccDNAs from human plasma and identified thousands of unique eccDNAs1, supporting the applicability of MDA in our embryonic profiling.\n(2) False positive control strategies\n①We implemented several strategies in our analysis pipeline to minimize potential false positives, as described in the “Methods” section (Lines 814–819). To ensure the quality of identified eccDNAs, we used a masked reference genome where interspersed repeats and low-complexity regions were masked by replacing repeats with ‘N’s. Additionally, we applied the ENCODE blacklist regions (https://github.com/Boyle-Lab/Blacklist/) for further filtering. EccDNAs mapped to mitochondrial or uncertain contigs (e.g., chrGL*, chrJH*, chrMT) were also excluded.\n②In downstream analyses, we mainly focused on relatively short eccDNAs to improve accuracy by applying strict thresholds: fragment sizes were limited to 100 bp - 1 Mb, and each eccDNA was required to have at least 1 split read and a combined total of ≥4 split plus discordant reads. These criteria enhance detection specificity.\n(3) Experimental validation support\nWe randomly selected several candidate eccDNAs and validated their circular structure by outward PCR followed by Sanger sequencing. These experiments support the accuracy of our eccDNA identification (Figure S1D; Figure 5E).\n(4) Low frequency of structural variants during preimplantation stages and minimal overlap with eccDNAs\nPrevious studies have established that early embryos maintain genome stability, with relatively few structural variants (SVs) occurring during development2. To rigorously exclude the possibility that our detected eccDNAs were confounded by structural variations, we performed SV calling across all stages using the LUMPY tool3. Our analysis confirmed a low burden of SVs, including inversions, translocations, deletions, and duplications (Figure S7D), and overlap analysis showed minimal intersection between SVs and eccDNAs (Figure S7E). These new results have been added to the revised manuscript and are now described in the main text (Lines 574-581).\n2. Regarding the quantification of eccDNAs.\nThe study introduces a sound quantification method for eccDNA, with an attempt to measure the eccDNA abundance. The current method introduces factor r to adjust for the length of concordant reads. Since the equation is now Ei = r*Ci + Si + Di, would the discordant reads increase with the eccDNA size? Which means that large eccDNA will be quantified as “high copy number”? Please check if there is a correlation between the size and quantification eccDNA to exclude the potential effect.\nRE: Thank you for your valuable comments. As suggested, we performed correlation analyses between eccDNA size and its quantified abundance at each developmental time point. The correlation coefficients (ρ) are shown within each panel of Figure S4I. Our analysis revealed no consistent or strong correlation between eccDNA size and abundance across all time points, suggesting that the quantification is not systematically biased by eccDNA length. The updated results have been incorporated into the revised manuscript (Lines 814-819).\n3. Regarding the origin of eccDNAs detected in the embryos.\nThe data regarding the eccDNA counts presented in Figure 1B provides a very interesting pattern that the authors might want to evaluate further. That is the origin of these large amount of embryonic eccDNAs.\nInstead of originating from the fertilized maternal and paternal genomes, these increasingly large amounts of eccDNAs might come from the copy of “discarded” maternal polar body. This will explain why there are significantly higher number of eccDNA found in the 1C, 2C, and 4C stages.\nRationally, it is quite logic to postulate that if the eccDNA are derived from the fertilized genomes, they will create corresponding deletion scars, leading to permanent genomic deletions in the subsequent cells. Depending on the heterozygosity of the sperm and oocytes, it might be possible and interesting to determine the eccDNAs derived from paternal and maternal (polar body) genomes. Alternatively, crossing oocyte and sperm from two strains (with diverse SNP) should be able to address.\nThe second polar body contributes embryo development. However, most studies have been focused on the carry-on transcripts and proteins. Since the genome of second polar body will go into the apoptosis processed, which is directed linked to eccDNA formation. Addressing this link will significantly increase the novelty and findings of this study.\nRE: We sincerely thank the reviewer for this insightful question. We have addressed this issue as follows:\n(1) Potential contamination from polar body-derived eccDNAs\nMII oocytes and polar bodies share nearly identical genomic content due to their origin from the same meiotic division. Given this, we reanalyzed unpublished MDA-based whole-genome sequencing data from MII oocytes generated by our group using the same analytical pipeline. The analysis revealed minimal overlap between eccDNAs detected in MII oocytes and those identified at various embryonic stages in the present study (Figure S7B). Although the proportion of MII- or polar body-derived eccDNAs slightly increases from the 8-Cell stage onward, they constitute only a minor fraction and do not affect the main conclusions of our study. Given that our primary focus is on early embryonic development, particularly around the 2-Cell stage, potential contamination from MII- or polar body-derived eccDNAs is likely negligible (see details in the revised main text, Lines 561-570).\n(2) Relationship between eccDNA formation and genomic deletions\nRegarding the theoretical expectation that eccDNA formation from the fertilized genome should leave deletion scars, we also performed structural variation (SV) detection on embryonic samples using LUMPY3. Our analysis revealed very few chromosomal deletions across developmental stages (Figure S7D). Moreover, the overlap between detected SVs and eccDNA regions at each stage was minimal (Figure S7E). Therefore, we infer that most eccDNAs do not arise from large-scale chromosomal deletions. A detailed description of this analysis is included in the revised main text (Lines 574-581).\nThis finding is consistent with existing mechanistic studies suggesting that eccDNA formation is not restricted to extensive DNA breakage and recombination but may also arise through replication errors (e.g., replication slippage) or DNA repair pathways such as non-homologous end joining (NHEJ) or microhomology-mediated end joining (MMEJ) without causing significant deletions4, 5. For example, the “episome model” proposes that replication fork collapse or stalling during DNA replication can lead to excision of replication bubbles from chromosomes, forming circular DNA that can then be amplified as eccDNA.\n(3) Future directions\nWe fully agree with the reviewer’s suggestion that crosses between mouse strains with distinct SNP backgrounds can be employed to trace eccDNA parental origins by leveraging maternal and paternal SNP differences. This approach will help clarify whether eccDNAs derive from maternal polar bodies, paternal genomes, or post-fertilization recombination events. So we plan to adopt such designs in future studies combined with single-cell eccDNA analyses to better elucidate the true origins and potential functions of eccDNAs during early embryogenesis.\n4. Regarding the statistics of overlapping eccDNA with ZGA genes.\nA few analyses in the current study will benefit from more statistic analyze to acutely reflect the significance.\nFor examples, there are 1229 ZGA genes overlap with the eccDNA-associated genes gained in the 2-cell stages. It is important to test whether this is overrepresented than by chance. The author can randomly subset same number of genes from the mouse genome (and from the non-ZGA genes), and carry out similar overlapping analysis, following by testing the enrichment with Fisher exact test. This is important to test whether the eccDNA found at the 2-Cell stage are more enriched to ZGA genes than other genes.\nSimilarly, same testing should be applied to Figure 3G, Figure 4D, Figure 5D. In Figure 5D, there are 78 genes overlap between eccDNA+ and ZGA genes. If you randomly subset the same number of genes as eccDNA+ genes and see how many of these will overlap with ZGA genes by chance.\nRE: We sincerely appreciate the reviewer’s valuable suggestions. In each case, we performed enrichment analyses by randomly sampling equal-sized gene sets from either the whole genome or the non-ZGA gene set as controls, followed by Fisher’s exact tests. Our results consistently revealed a significant overlap between eccDNA-associated genes and ZGA gene set, which was not observed in random controls. These analyses have been incorporated into the revised figures and main text, as follows: Figures S1B–S1C (Lines 159–165), Figure S3E (Lines 314–322), Figures 5D and S4C–S4D (Lines 388–393), and the revised Figure 6D together with the new Figure S4E (Lines 428–430).\nOther minor comments\n1. Abstract. It is still quite odd to call eccDNA as a novel epigenetic regulatory element. Small eccDNA comprising cis regulatory element could regulate distance gene expression, but not via the classical epigenetic path.\nRE: We thank the reviewer for pointing this out. Accordingly, we have revised the text in the Abstract to refer to eccDNA as “an emerging regulatory element” (Line 33) to more accurately reflect its potential role in gene regulation without implying classical epigenetic mechanisms.\n2. Figure 1. A, why the 8C image is smaller than others. B. what are the number of eccDNAs same between rep 1 and rep2? C. provide the value of bp for the eccDNA peaks; G. value provided for the length of eccDNA peaks in figure.\nRE: We thank the reviewer for these helpful suggestions. We have corrected the figure panel layout. The number of eccDNAs shared between replicate 1 and replicate 2 is now shown in the revised Figure S1A, and a full list of the shared eccDNAs has been provided as Supplementary Table 1. The corresponding text in the manuscript has also been updated (Lines 142–144): “The number of eccDNAs exhibiting complete overlap between the two replicates at each stage is shown in Figure S1A and Supplementary Table 1.” In addition, we have added the specific values for the peak sizes (in base pairs) and the eccDNA length distributions directly in the figure panels as requested.\n3. Figure 2. B. texts (length of direct repeats) should be placed at the right position. The pie chart values are fractions. D, are the TF motifs only detected for 2C eccDNAs?\nRE: We thank the reviewer for these helpful comments. For Figure 2B, we have revised the texts to “Distribution of eccDNA abundance by direct repeat length” Regarding Figure 2D, most transcription factor (TF) motifs were specifically enriched at the 2-Cell stage. However, a few motifs, such as FOXO1 and SOX15, were also detected at later stages.\n4. Figure 3. Re-order that figures so that sub figures will appear later. D and G were above C and E, making it hard to follow.\nRE: We thank the reviewer for pointing this out. We have corrected the order of the sub figures in Figure 3 so that they now appear in a logical sequence (A to G).\n5. Related to last part of eccDNA and human developmental disorders, the current data and results are not sufficient to draw this conclusion. This is because the eccDNAs derived from ATAC-seq are already bias to gene regions. Furthermore, since most eccDNAs are exhibiting high diversity and different from stages to stages, the interpretation will change depending on the eccDNAs from what stages.\nRE: We thank the reviewer for this insightful comment. In response, we have revised the relevant sentence to avoid over-interpretation and better reflect the limitations of our current data. Specifically, we now emphasize the potential association rather than suggesting a direct causal link, and acknowledge the stage-specific diversity and gene-region bias introduced by ATAC-seq based eccDNA identification. The revised text is included in the main manuscript (Lines 508-528) as follows: “Human developmental disorders, such as autism spectrum disorder (ASD), are strongly influenced by genetic and epigenetic perturbations. Numerous large-scale sequencing studies have demonstrated that de novo mutations (DNMs) play a major role in ASD etiology. Meanwhile, eccDNA has been increasingly recognized as an important contributor to gene regulation and chromatin organization, with potential impacts on genome stability and cellular processes. To investigate whether eccDNAs may intersect with genomic regions relevant to neurodevelopmental pathogenesis, we analyzed the overlap between eccDNA regions and DNMs implicated in ASD. DNMs associated with ASD were obtained from the Gene4Denovo database. Our analysis revealed that ASD-related DNMs significantly overlapped with eccDNA identified in human preimplantation embryos, compared with simulated random controls (rate ratio = 1.6780, P value < 0.001; odds ratio = 2.9072, P value < 0.001) (Figure 7E). Although this observation suggests a non-random spatial association between eccDNA-forming regions and DNMs involved in neurodevelopmental disorders, the eccDNAs analyzed in this section were identified based on ATAC-seq data, which inherently biases detection toward open chromatin regions enriched for gene activity. Additionally, eccDNA populations are highly heterogeneous across developmental stages. Further investigation using stage-specific eccDNA datasets and functional validation will be essential to determine whether eccDNA formation contributes to the etiology of human developmental disorders.”\nWe hope this revision sufficiently addresses the reviewer’s concern and improves the clarity and accuracy of our interpretation."}, {"round": 2, "reviewer_comments": {"reviewer_1": "In the revision, the authors have done an excellent work to address the comments and suggestions in responding to the questions that I have raised in the previous evaluation. I have carefully gone through both main and supporting figures, which are structured and presented clearly. The study has provided very unique mechanistic insights to the roles of eccDNAs in early development. Considering the highly competitive nature of the field (e.g., the new ecDNA study published in Nature on Nov 19) and the great interests provided by the current study, I recommend the publication of the study.", "reviewer_2": "The authors have addressed most of my previous concerns, and the manuscript has been substantially improved. Two minor issues need to be addressed before the manuscript can be considered for publication.\n1. In line 90-99, the functions of extrachromosomal circular DNA cited in the Introduction—such as oncogene overexpression—and the references (ref 12-14) used in this section primarily describe large ecDNA fragments (>10 Mb, containing oncogenes and capable of being inherited). These are conceptually and biologically distinct from the small eccDNA populations discussed throughout the manuscript (those present in human sperm, healthy tissues, and cancers). This creates ambiguity and may mislead readers regarding the functional relevance of the eccDNA studied here. I recommend that the authors clearly delineate the existing background knowledge specific to the type and size class of extrachromosomal circular DNA investigated in this work, and avoid extrapolating functional attributes from large ecDNA that are not applicable to small eccDNA.\n2. Lines 189–192: The manuscript states that certain genomic features of eccDNAs—such as chromosomal distribution and enrichment in repetitive elements (e.g., ALUs)—are consistent with previous reports. However, your analysis shows no correlation (R = 0.099, P = 0.67), which contradicts the established literature. This discrepancy requires explanation. Moreover, describing R = 0.099 with P = 0.67 as “a weak positive correlation” is inaccurate; statistically, this result should be considered non-correlated. Please revise the description and provide a justification for the inconsistency with known findings.", "reviewer_4": "The authors in their revised manuscript addressed some of the issues raised by this reviewer. However, the main two issues remain unresolved.\n1.⁠ ⁠The authors seem to suggest that most of the work in the field of mouse preimplantation development is done using IVF obtained embryos, which is not true. Obtaining fertilised zygotes, 2-cell, 4-cell and blastocyst stage can be easily done and is a widely accepted baseline for investigating any processes in preimplantation development (see work from Rossant, Chazaud, Zernicka-Goetz, Hiiragi and other labs. Without seeing data from the freshly collected embryos there is no way to properly assess how closely the data presented by the authors reflect situations in normal development (especially that it is widely accepted that mouse IVF embryos tend to accumulate more DNA damage and more frequently exhibit some anomalies in gene expression than embryos after natural mating)\n2.⁠ ⁠ In the functional experiments, although the authors demonstrated that Mycn and Egfl7, mRNA levels increased upon injection of synthesized eccDNA molecules, however, no further effect on embryo viability, development to the blastocyst stage, correct lineage formation etc was investigated. It is still not clear whether increase in Mycn and Egfl7 mRNA levels translates anyhow to improve/decrease of developmental competency. Without such proof, the importance of the finding presented by the authors remains unclear.\nMinor point\nIntroduction Line 102-103 sentence „species are rapid cleavage divisions and the transition from maternal to zygotic control of gene expression” still refers to “rapid cleavage divisions despite the authors reply to one of the reviewers that they appreciate that early cleavages are not “rapid” (first cell cycle is about 24h, second 12h etc)."}, "author_response": "RE: We sincerely thank the reviewer for the positive evaluation and constructive feedback. We greatly appreciate the reviewer’s careful assessment of the revised manuscript and figures, as well as the recognition of the significance and mechanistic insights provided by our study. We are grateful for the reviewer’s recommendation for publication.\nRE: We thank the reviewer for the insightful suggestion. In the revised manuscript, we have thoroughly rewritten the background section in the Introduction to avoid conflating these two biologically distinct entities. The revised text (lines 90-100) now reads as follows: “The role of large, megabase-sized ecDNA in cancer biology is well-established. These molecules exhibit highly open chromatin structures that potently activate the transcription of carried oncogenes12. This, in turn, drives tumor progression by promoting oncogene overexpression, enhancing genomic heterogeneity, and enabling drug resistance through gene amplification13, 14, 15. In parallel, a distinct population of small eccDNAs has been identified in both normal biological and pathological conditions5, 16. These molecules play diverse roles, including regulating gene expression, participating in DNA damage repair mechanisms10, and serving as potential biomarkers for disease detection and monitoring17. Notably, these small species are abundantly generated in the germline, as evidenced by their extensive profiles in both humans and mice8,18.”\nRE: We thank the reviewer for raising this important point. We have removed the inaccurate description and revised the relevant text accordingly (lines 190–200), as shown below: “Next, we evaluated potential correlations between eccDNA frequency and genomic features. No correlation was identified between eccDNA and gene density (Figure S1G, left; R = 0.099, P value = 0.67). A moderate positive correlation was observed between eccDNA and Alu element density although it was not statistically significant (Figure S1G, right; R = 0.402, P value = 0.071). Previous studies have reported variable relationships between eccDNA frequency and genomic features such as gene density and Alu element content6, 9. In our dataset from early preimplantation embryos, eccDNA frequency did not exhibit a significant correlation with these genomic features. These differences are likely attributable to cell-type-specific mechanisms of eccDNA formation and to differences in genomic hotspots of eccDNA generation”."}, {"round": 3, "reviewer_comments": {"reviewer_1": "In revised of the revised manuscript by Wei L. et al., the suggested comments in response to the aspect of LOH has been adequately addressed in the discussed texts. I have also make a proof reading again for the manuscript. The following minor changes should be considered where suitable. Considering the novelty might be affected by the completing publication time. I don't think another round of revision seen by me is necessary. I recommend the publication of the study after addressing this reminding minor points.\nFigure 3A, Text font for this figure seems different from others. Please correct.\nLine 287-288 and line 329-330. The sentence should be heading of the section instead of the first sentence of the paragraph.\nMinor point regarding the selection of words, e.g. line 378, \"delve deeper into\", this has been a pretty over used word by language models like ChatGPT. If AI tool has been used for language polishing and required according to the journal publication policy, the use of AI tool should be disclosed.", "reviewer_2": "All my concerns have been nicely addressed.", "reviewer_4": "While I appreciate that the technical difficulties may restrict the author ability to address some of the major problems of this manuscript, this, however, does not change a fact that the importance of this work for the wider audience (beyond the work on eccDNAs) is still unclear. Moreover, without comparison to the in vivo embryos there is no way to say how much of that may or may not be the artefact of the IVF. While I accept the authors reasoning behind using IVF cultured embryos (which is partially valid), it is equally important to establish that what the authors observed is NOT a culture artefact. In my opinion, looking at the freshly flushed embryos is necessary to assess the importance of this work."}, "author_response": "RE: Thank you for pointing this out. The font in Figure 3A has been corrected to ensure consistency with the other figures.\nRE: We thank the reviewer for the insightful suggestion. We have revised the manuscript accordingly and now the sentences are formatted as section headings.\nRE: We thank the reviewer for the suggestion. The phrase “delve deeper into” has been replaced with “To investigate”. The AI tool was used only for grammar correction and readability improvement, which does not require disclosure according to Springer Nature’s policy.\nRE: Thank you for your insightful comments and valuable suggestions. We have carefully considered your concerns and have added the following supplementary explanations in the revised manuscript:\n1) mentioning that the embryos used in the study are IVF-derived in the first results subsection,\nWe have now clearly stated in the first results subsection that all embryos used are IVF-derived. The sentence now reads: “...we carried out extensive genomic sequencing using MDA on IVF-derived embryos across six pivotal stages of mouse preimplantation development...”\n2) mention any precautions taken to diminish the concern\nWe have expanded the Discussion to explain the precautions taken to minimize potential biases associated with the use of IVF-derived embryos ( Lines 650-653). Embryos were cultured under a standardized, optimized protocol and only morphologically normal embryos were included. In addition, embryos were collected at strictly matched developmental stages and processed in parallel to minimize experimental variation. Furthermore, the key features observed, such as the timing and pattern of ZGA-associated transcriptional activation, are highly consistent with previously reported data from both IVF and in vivo-derived embryos, suggesting that the observed patterns are unlikely to represent IVF-specific artifacts.\n3) discuss reasoning behind using IVF-derived embryos and potential limitations in the Discussion section.\nWe have expanded the Discussion to explain the rationale for using IVF-derived embryos and to acknowledge the associated limitations ( Lines 654-665). Our decision to use IVF embryos was driven by practical considerations, including the need for precise developmental staging and sufficient material for sequencing, requirements that are technically challenging to achieve with in vivo-derived embryos. Nevertheless, we acknowledge that naturally fertilized embryos represent the physiological baseline, and the lack of direct comparison with in vivo-derived embryos is a limitation. Future studies using in vivo flushed embryos will be important to validate and extend our findings."}]

Decision letter