Duplicate gene enrichment and expression pattern diversification in multicellularity
Bibliographic record
Abstract
The enrichment of duplicate genes, and therefore paralogs (proteins coded by duplicate genes), in multicellular versus unicellular organisms enhances genomic functional innovation. This study quantitatively examined relationships among paralog enrichment, expression pattern diversification and multicellularity, aiming to better understand genomic basis of multicellularity. Paralog abundance in specific cells was compared with those in unicellular proteomes and the whole proteomes of multicellular organisms. The budding yeast, Saccharomyces cerevisiae and the nematode, Caenorhabditis elegans, for which the gene sets expressed in specific cells are available, were used as uni and multicellular models, respectively. Paralog count (K) distributions [P((k))] follow a power-law relationship [Formula in text] in the whole proteomes of both species and in specific C. elegans cells. The value of the constant α can be used as a gauge of paralog abundance; the higher the value, the lower the paralog abundance. The α-value is indeed lower in the whole proteome of C. elegans (1.74) than in S. cerevisiae (2.34), quantifying the enrichment of paralogs in multicellular species. We also found that the power-law relationship applies to the proteomes of specific C. elegans cells. Strikingly, values of α in specific cells are higher and comparable to that in S. cerevisiae. Thus, paralog abundance in specific cells is lower and comparable to that in unicellular species. Furthermore, how much the expression level of a gene fluctuates across different C. elegans cells correlates positively with its paralog count, which is further confirmed by human gene-expression patterns across different tissues. Taken together, these results quantitatively and mechanistically establish enrichment of paralogs with diversifying expression patterns as genomic and evolutionary basis of multicellularity.
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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 | 0.001 |
| 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".