Evolution of chromatin accessibility associated with traits of cichlid phenotypic diversity
Bibliographic record
Abstract
Abstract The radiations of cichlid fishes in East African Lakes Victoria, Malawi, and Tanganyika showcase a remarkable example of rapid adaptive speciation, with over 2000 species evolving diverse morphological and ecological adaptations within the last few million years. Understanding the molecular basis of this phenotypic diversity remains a key challenge. Building on prior evidence of gene regulatory network (GRN) rewiring underpinning adaptive traits, we profiled chromatin accessibility (ATAC-seq) and matched transcriptomes across forebrain, retina, liver, and testis tissues in five representative cichlid species using optimised protocols. We show extensive divergence in chromatin accessibility corresponding to phylogenetic lineages and tissue identity, with many regulatory regions exhibiting accelerated nucleotide evolution. Transcription factor binding site (TFBS) variation correlates with both chromatin accessibility and differential gene expression, particularly in genes linked to sensory systems. Building on this, we reconstructed tissue- and species-specific GRNs and show that motif-supported network inference reveals pervasive but tissue-dependent rewiring, with the strongest candidate edges concentrated among accessible, highly expressed genes linked to adaptive traits. By integrating TF footprinting with regulatory motif turnover analyses, we demonstrate that dynamic nucleotide changes are associated with GRN rewiring, concordant with ecological niche and lineage-specific adaptations. Our findings highlight that regulatory variation at conserved and novel TFBSs associates with genes linked to phenotypic innovation across radiating and non-radiating East African cichlids. This study provides foundational epigenomic evidence linking GRN divergence to key mechanisms facilitating rapid adaptive diversification in this iconic vertebrate radiation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".