Methylation specific targeting of a chromatin remodeling complex from sponges to humans
Bibliographic record
Abstract
DNA cytosine methylation and methyl-cytosine binding domain (MBD) containing proteins are found throughout all vertebrate species studied to date. However, both the presence of DNA methylation and pattern of methylation varies among invertebrate species. Invertebrates generally have only a single MBD protein, MBD2/3, that does not always contain appropriate residues for selectively binding methylated DNA. Therefore, we sought to determine whether sponges, one of the most ancient extant metazoan lineages, possess an MBD2/3 capable of recognizing methylated DNA and recruiting the associated nucleosome remodeling and deacetylase (NuRD) complex. We find that Ephydatia muelleri has genes for each of the NuRD core components including an EmMBD2/3 that selectively binds methylated DNA. NMR analyses reveal a remarkably conserved binding mode, showing almost identical chemical shift changes between binding to methylated and unmethylated CpG dinucleotides. In addition, we find that EmMBD2/3 and EmGATAD2A/B proteins form a coiled-coil interaction known to be critical for the formation of NuRD. Finally, we show that knockdown of EmMBD2/3 expression disrupts normal cellular architecture and development of E. muelleri. These data support a model in which the MBD2/3 methylation-dependent functional role emerged with the earliest multicellular organisms and has been maintained to varying degrees across animal evolution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".