Functional analysis of co-expression networks of zebrafish <i>ace2</i> reveals enrichment of pathways associated with development and disease
Bibliographic record
Abstract
Human Angiotensin I Converting Enzyme 2 (ACE2) plays an essential role in blood pressure regulation and SARS-CoV-2 entry. ACE2 has a highly conserved, one-to-one ortholog (ace2) in zebrafish, which is an important model for human diseases. However, the zebrafish ace2 expression profile has not yet been studied during early development, between genders, across different genotypes, or in disease. Moreover, a network-based meta-analysis for the extraction of functionally enriched pathways associated with differential ace2 expression is lacking in the literature. Herein, we first identified significant development-, tissue-, genotype-, and gender-specific modulations in ace2 expression via meta-analysis of zebrafish Affymetrix transcriptomics datasets (ndatasets = 107); and the correlation analysis of ace2 meta-differential expression profile revealed distinct positively and negatively correlated local functionally enriched gene networks. Moreover, we demonstrated that ace2 expression was significantly modulated under different physiological and pathological conditions related to development, tissue, gender, diet, infection, and inflammation using additional RNA-seq datasets. Our findings implicate a novel translational role for zebrafish ace2 in organ differentiation and pathologies observed in the intestines and liver.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".