Nuclease deficiencies alter plasma cell-free DNA methylation profiles
Bibliographic record
Abstract
The effects of DNASE1L3 or DNASE1 deficiency on cell-free DNA (cfDNA) methylation were explored in plasma of mice deficient in these nucleases and in DNASE1L3-deficient humans. Compared to wild-type cfDNA, cfDNA in DNASE1L3-deficient mice was significantly hypomethylated, while cfDNA in DNASE1-deficient mice was hypermethylated. The cfDNA hypomethylation in DNASE1L3-deficient mice was due to increased fragmentation and representation from open chromatin regions (OCRs) and CpG islands (CGIs). These findings were absent in DNASE1-deficient mice, demonstrating the preference of DNASE1 to cleave in hypomethylated OCRs and CGIs. We also observed a substantial decrease of fragment ends at methylated CpGs in the absence of DNASE1L3, thereby demonstrating that DNASE1L3 prefers to cleave at methylated CpGs. Furthermore, we found that methylation levels of cfDNA varied by fragment size in a periodic pattern, with cfDNA of specific sizes being more hypomethylated and enriched for OCRs and CGIs. These findings were confirmed in DNASE1L3-deficient human cfDNA. Thus, we have found that nuclease-mediated cfDNA fragmentation markedly affects cfDNA methylation level on a genome-wide scale. This work provides a foundational understanding of the relationship between methylation, nuclease biology, and cfDNA fragmentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".