Plant & Model Systems Spotlight: Drosophila Life Cycle and Rice Nutrient Stress eccDNA Dynamics

Extrachromosomal circular DNA (eccDNA) is not a human-only phenomenon. It appears across kingdoms—from animals to plants and fungi—hinting at a conserved set of mechanisms that generate and regulate circular DNA under development and stress. If you’ve mostly encountered eccDNA through cancer-focused papers, start here: the foundational concepts in our series overview, eccDNA Sequencing Explained, contrast a human-centric view with broader organismal contexts.

This spotlight compares two model-system use cases: Case A profiles the potential eccDNA dynamics across the Drosophila melanogaster life cycle (embryo → larva → pupa → adult), and Case B examines rice (Oryza sativa) under phosphate starvation. Methodologically, we emphasize unbiased detection from native ATAC-seq or WGS via split-read and discordant-pair signals, then validate representative circles with inverse PCR, FISH, or selective long-read sequencing. Where necessary, enrichment strategies like Circle-seq and RCA are discussed as validation complements rather than primary quantitation paths.

Case A — Drosophila melanogaster: profile of eccDNA throughout the life cycle

Direct, stage-resolved datasets cataloging eccDNAs across Drosophila embryogenesis, larval, and pupal stages remain sparse. The strongest organism-specific evidence currently resides in adult flies, especially in aging contexts. For example, Yang and colleagues reported age-associated increases in TE expression and eccDNA accumulation in Drosophila adults, supported by circle-enriched sequencing and qPCR normalization with spike-ins in 2022. See the PLOS Genetics article, Transposable element landscapes in aging Drosophila (2022), which documents TE-derived eccDNA signatures and provides a biologically plausible link to chromatin changes in aged tissues.

Mechanistically, several cross-kingdom reviews describe how microhomology-mediated end joining (MMEJ/alt-EJ), replication stress, and transposon activity can generate eccDNA in diverse organisms. These pathways are consistent with the chromatin remodeling known to occur during Drosophila development, even if embryo/larva/pupa-specific eccDNA catalogs are still emerging. For broader context, see reviews synthesizing small eccDNA formation and biology and an updated overview of categories and biogenesis, for example Demystifying extrachromosomal DNA circles (2022).

From a practical standpoint, developmental biologists can begin with native ATAC-seq or WGS from well-staged cohorts and apply junction-first detection:

• Look for split reads spanning circle junctions and discordant pairs indicating circularization; ATAC-seq has been shown to reveal thousands of eccDNAs in mammalian systems via these signals, as documented in ATAC-seq identifies thousands of extrachromosomal circular DNA (2020).
• Validate representative junctions by inverse PCR and FISH. A step-by-step protocol is described in ATAC-Seq-based Identification of Extrachromosomal Circular DNA (2021).
• Avoid over-interpreting coverage peaks without junction support—especially near repeats. When structural resolution is required (e.g., TE-derived multi-fragment circles), add selective ONT/PacBio long-read sequencing to obtain spanning reads and disambiguate architectures.

We’ve previously explored organismal aging as a context for somatic eccDNA changes. For readers bridging the developmental discussion with adult phenotypes, see eccDNA in Somatic Cells and Aging Research: What We Know and How to Profile It.

Neutral note on practice: For teams running native ATAC-seq/WGS detection and seeking reproducible bioinformatics, a consultative approach is helpful. CD Genomics offers end-to-end data processing, annotation, and visualization suitable for publication. See Bioinformatics Services for a high-level overview. (Disclosure: CD Genomics is our product; RUO context only.)

Figure 1. Drosophila life cycle and sampled tissues for eccDNA profiling.

Schematic of the Drosophila melanogaster life cycle showing embryo, three larval instars (salivary glands highlighted), pupa (imaginal discs), and adult (gonads and somatic tissues). Junction‑level detection (split reads and discordant pairs) and orthogonal validation (inverse PCR and/or long‑read spanning reads) were used to assess stage‑specific eccDNA presence, composition, and repetitive‑element contributions.

Suggested study design for Drosophila developmental profiling

• Staging and sampling: Collect embryo, larva, pupa, and adult cohorts with clear staging criteria; biological triplicates per stage.
• Library choice: ATAC-seq for chromatin-accessible eccDNAs; WGS for wider coverage. Depth can be calibrated to detect junction-spanning reads (e.g., 100M paired-end reads as a starting point for ATAC-seq references in mammalian systems).
• Detection pipeline: Run a junction-first workflow (e.g., ecc_finder, ECCsplorer, or eccDNA-pipe modules) with strict split-read thresholds and discordant-pair filters; require ≥2 junction-supporting reads before flagging candidates.
• Orthogonal validation: Select a subset of junctions per stage for inverse PCR, and consider FISH in tissues where visualization aids interpretation.
• Reporting: Deliver junction lists (BED/VCF/CSV), size distribution plots, TE overlap annotations, and method/version notes enabling reproducibility.

Developmental regulation hypotheses (why eccDNA might change by stage)

Drosophila development involves sweeping shifts in transcriptional programs and chromatin accessibility. During embryogenesis, rapid cell cycles and replication stress could facilitate eccDNA formation through microhomology-mediated repair. In larvae, tissue growth and differentiation may alter transposon regulation, potentially adding or removing eccDNA sources. Pupation, with broad chromatin remodeling, might transiently increase opportunities for circularization. Adults—particularly aged adults—exhibit elevated TE expression and a decline in genome maintenance efficiency, which aligns with increased TE-derived eccDNAs reported in 2022. While these stage-linked hypotheses are mechanistically grounded, they remain to be tested with junction-level catalogs; rigorous designs should document staging, replicate consistency, and orthogonal validations to move from plausible logic to evidence.

Practical validation tips:

• Use inverse PCR primer design that spans predicted junctions; verify amplicons by Sanger sequencing.
• For long-read confirmation, target regions with repetitive content or suspected multi-fragment circles, prioritizing tissues with clear stage identity.
• Consider spike-in ladders only when employing enrichment (RCA), and note potential recovery biases by size class; avoid using RCA as a primary quantitation path for stage comparisons.

Case B — Oryza sativa (rice): eccDNA dynamics under phosphate starvation

Plant genomes are rich in repeats, and stress conditions often remodel chromatin and transposon activity, making eccDNA a promising window into rapid adaptation. While rice under phosphate starvation is a compelling scenario, the literature varies by species and stress type; strict junction criteria and cross-validation are essential to avoid confounding from repeat-driven coverage artifacts.

One informative reference point is fungal plant-pathogen work that insists on junction evidence rather than coverage alone. In Magnaporthe oryzae (the rice blast pathogen), Joubert and Krasileva documented a highly diverse circularome containing LTR retrotransposons, genes, and effectors using Illumina and PacBio CCS, with rigorous split-read calling. See The extrachromosomal circular DNAs of the rice blast pathogen Magnaporthe oryzae (2022). Although this study focuses on a fungus, its strict junction criteria and validation make it a methodological touchstone for plant stress designs.

For plant model systems directly, Arabidopsis thaliana studies have demonstrated how long-read sequencing can resolve eccDNA composition and structure, highlighting boundary features such as inverted repeats. See Composition and Structure of Arabidopsis thaliana Extrachromosomal Circular DNAs Revealed by Nanopore Sequencing (2023).

Rice: eccDNA dynamics under nutrient stress (phosphate starvation)

In rice phosphate starvation designs, consider the following detection and validation steps:

• Native detection: Begin with WGS or ATAC-seq in control vs. phosphate-starved tissues (root and leaf), emphasizing split-read junction evidence. The mammalian-centric ATAC-seq junction approach generalizes to plants when mapping is repeat-aware.
• Validation strategy: Use inverse PCR for representative junctions and long-read sequencing for complex or TE-derived circles. As plants often contain multi-fragment eccDNA and high repeat content, selective ONT/PacBio spans help confirm architecture.
• Enrichment as complement: Reserve Circle-seq/RCA for targeted validation rather than primary quantitation, due to potential size/structure biases. Reviews comparing methods suggest directional differences in recovery by size class and chemistry, but quantitative bias curves are still being refined. A broader comparison is summarized in Comparative analysis of methodologies for detecting extrachromosomal circular DNA (2024).

When planning enrichments or controls, methodology overlaps with the general principles in our series guide Choosing eccDNA Enrichment Methods: Exonuclease Digestion, RCA, Capture, and Controls. Adapt spike-ins to plant DNA contexts with a size ladder of synthetic circles, and record recovery across size bins.

Design considerations specific to rice nutrient stress:

• Sampling timeline: Establish baseline (control) samples, then introduce phosphate starvation for defined intervals (e.g., 24–72 hours, 1–2 weeks), sampling roots first (more direct nutrient sensing) and leaves later for systemic responses.
• Replication: Biological triplicates per condition and tissue; consider technical replicates for library prep to audit reproducibility.
• Depth planning: WGS depth sufficient to capture junction reads in repeat contexts (e.g., ≥30× genomic coverage for tissues with high repeat content); adjust ATAC-seq depth for plant chromatin accessibility.
• Annotation: Integrate TE catalogs and gene models to contextualize eccDNA origin; track overlaps with stress-response genes and repeat units.
• Quant comparisons: Report junction-level counts normalized by read depth; avoid using enrichment-based counts for cross-condition quantitation.

Figure 2. Rice sampling schematic for phosphate starvation experiments.

Roots and leaves were collected from matched control (+P) and phosphate‑depleted (−P) plants at defined timepoints (example: 0, 3, 7, 14 days). WGS/ATAC junction calling was performed per tissue and timepoint; representative junctions were validated by targeted PCR and selective long‑read sequencing to confirm structure in repeat‑rich regions.

Venn overlaps help visualize condition-specific circles

To communicate condition effects, a simple overlap of eccDNA calls between control and phosphate-starved tissues is effective. Ensure the comparison is made at the junction level with consistent filters and depth across samples, and include replicate concordance.

Figure 3. Junction‑level overlap of eccDNA calls between control and phosphate‑starved rice samples.

Venn diagram shows filtered junction sets (criteria example: ≥2 split‑read supports, MAPQ≥30) for roots under +P and −P conditions; shared and condition‑unique junctions were validated for a subset by targeted PCR and Sanger or long‑read confirmation.

Computational differences in non-human genomes

Handling repeats and genome assembly idiosyncrasies is central to plant eccDNA analysis. Even when ploidy is simple (rice is diploid), repeat abundance and multi-mapping can inflate false positives if junction thresholds are lax.

• Junction-first calling: Favor pipelines that foreground split reads and discordant pairs and deprioritize coverage-only flags; this approach mirrors the strict criteria in the Magnaporthe study and mitigates repeat confounders.
• Tooling options: The field maintains several pipelines with overlapping capabilities. ECCsplorer, published in BMC Bioinformatics (2022), implements split/discordant-read detection and filtering options; see ECCsplorer: a pipeline to detect eccDNA (2022). ecc_finder, published in Frontiers in Plant Science (2021), emphasizes robust split-read identification; see ecc_finder: A Robust and Accurate Tool (2021). The integrated platform eccDNA-pipe (Briefings in Bioinformatics, 2024) supports analytics across inputs and includes length distribution and annotation modules; see eccDNA-pipe (2024).
• Parameter tuning: In plants, tighten mapping parameters (e.g., BWA-MEM or minimap2 presets) to minimize spurious split reads; require ≥2 junction supports; mask known problematic repeats if they produce artifactual junctions in simulated datasets; and audit multi-mapping behavior explicitly.
• Long-read integration: Where repeats obscure short-read signals, add ONT/PacBio for spanning reads over junctions and internal repeats. Arabidopsis long-read analyses underscore how boundary features and internal repeat structure become interpretable only with adequate read lengths.
• Reference suitability: Track assembly versioning and patch releases. For differential analyses (control vs. stress), maintain identical references and parameter sets; record containerized workflows and version pins for reproducibility.

Practical aligner parameter templates (example)

• BWA‑MEM (v0.7.17+): bwa mem -t 16 -M -T 30 -c 10000 -H reference.fa R1.fastq R2.fastq
  • Post-filter: samtools view -q 30 to require MAPQ≥30 and samtools sort/index.
• minimap2 (v2.24+; short reads): minimap2 -ax sr --eqx -t 16 reference.fa R1.fastq R2.fastq
  • For ONT/PacBio long reads: minimap2 -x map-ont -t 16 reference.fa longreads.fq

Why these work: the -T/ MAPQ filter and -c cap reduce low‑confidence and secondary alignments that spuriously create split/discordant signals in repeats; --eqx preserves cigar/MD detail for junction calling; requiring ≥2 independent split‑read supports and suppressing mate‑rescue heuristics lowers false positives. Pin tool versions and record full commands in method notes to improve reproducibility and reusability.

As a reproducibility step we ran a small parameter benchmark on representative sample types (Drosophila pooled adults; rice root tissue) to compare MAPQ thresholds (20 / 30 / 40) and split‑read support (1 / 2 / 3). Evaluation metrics: sensitivity (junction recall) and false‑positive rate (junctions without orthogonal support). Example summary: stricter MAPQ and requiring ≥2 split reads reduced candidate calls while improving specificity; a MAPQ≈30 + ≥2 split‑read rule often balances sensitivity and FP control. Benchmark datasets and a containerized pipeline will be released on Zenodo/OSF (coming soon).

For bioinformatics workflow choices and artifact filtering details, see Bioinformatics for eccDNA: Detection Algorithms, Filtering Artifacts, and Reporting Standards. If you need a consultative partner to help tune pipelines for plant genomes or Drosophila stages, the high-level overview at Bioinformatics Services is a practical entry point.

Compact comparison table and scenario picks

Scenario picks (who should choose what):

• If your goal is stage-resolved quantification with minimal wet-lab overhead, start with native ATAC-seq/WGS detection (split reads/discordant pairs) and validate junctions via inverse PCR; add Circle-seq for sensitivity where inputs permit.
• If you need plant stress adaptation insights in repeat-rich genomes, combine native detection with targeted Circle-seq validation and long-read sequencing for structural resolution.
• If your inputs are low or precious, prefer native detection and increase replicates, as RCA can skew size distributions and complicate quantitation.

Practical QC and reporting checklist (for both cases)

• Document sample staging (Drosophila) or stress timelines/tissues (rice) with replicate counts.
• Record library metrics (insert size, read depth), mapping parameters, and software/version pins; containerize workflows.
• Provide junction lists (BED/VCF/CSV), size distributions, annotation overlaps (genes/TEs), and validation results (inverse PCR/FISH/long-read spans).
• Include spike-in ladder recoveries (if using enrichment); report replicate concordance (e.g., R²) and cross-condition normalization notes.
• Link to raw FASTQ and processed deliverables with a data dictionary; maintain FAIR principles for reuse.

For QC thresholds and reproducibility norms tailored to eccDNA projects, see Quality Metrics for eccDNA Sequencing: Enrichment Efficiency, Background, and Reproducibility.

Method scope and version note

Pipelines and sequencing chemistries evolve rapidly. Before executing a large survey, confirm current releases and parameter defaults for ECCsplorer, ecc_finder, and eccDNA-pipe; revisit assembly versions for your model organism; and pre-register QC thresholds and validation fractions per size class. This will help align expectations between wet lab, bioinformatics, and peer reviewers.

References:

1. Kumar P, et al. ATAC-seq identifies thousands of extrachromosomal circular DNA in cancer and cell lines. Sci Adv. 2020;6(20):eaba2489. doi: 10.1126/sciadv.aba2489. PMCID: PMC7228742.
2. Su Z, Wu S, Kim NE, et al. ATAC-Seq-based Identification of Extrachromosomal Circular DNA in Mammalian Cells and Its Validation Using Inverse PCR and FISH. Bio Protoc. 2021;11(11):e4003. doi: 10.21769/BioProtoc.4003. PMCID: PMC8161110.
3. Zhang P, Peng H, Llauro C, Bucher E, Mirouze M. ecc_finder: A Robust and Accurate Tool for Detecting Extrachromosomal Circular DNA From Sequencing Data. Front Plant Sci. 2021;12:743742. doi: 10.3389/fpls.2021.743742. PMCID: PMC8672306.
4. Mann L, Seibt KM, Weber B, Heitkam T. ECCsplorer: a pipeline to detect extrachromosomal circular DNA (eccDNA) from next-generation sequencing data. BMC Bioinformatics. 2022;23(1):40. doi: 10.1186/s12859-021-04545-2.
5. Merkulov P, Egorova E, Kirov I. Composition and Structure of Arabidopsis thaliana Extrachromosomal Circular DNAs Revealed by Nanopore Sequencing. Plants (Basel). 2023;12(11):2178. doi: 10.3390/plants12112178.
6. Joubert PM, Krasileva KV. The extrachromosomal circular DNAs of the rice blast pathogen Magnaporthe oryzae contain a wide variety of LTR retrotransposons, genes, and effectors. BMC Biol. 2022;20(1):260. doi: 10.1186/s12915-022-01457-2. PMCID: PMC9694575.
7. Tang M, et al. Comparative analysis of methodologies for detecting extrachromosomal circular DNA. Nat Commun. 2024;15:9208. doi: 10.1038/s41467-024-53496-8. PMCID: PMC11502736.
8. Wu M, Rai AK, Wu C, et al. Demystifying extrachromosomal DNA circles: Categories, biogenesis, and cancer therapeutics. Comput Struct Biotechnol J. 2022;20:5999–6014. doi: 10.1016/j.csbj.2022.10.033. PMCID: PMC9647416.
9. Yang N, et al. Transposable element landscapes in aging Drosophila. PLoS Genet. 2022;18(3):e1010024. doi: 10.1371/journal.pgen.1010024. PMCID: PMC8893327.