The Power of an Evolutionary Perspective in Studies of Endocrinology
Bibliographic record
Abstract
Much of our understanding of the molecular basis of endocrinology has been the product of highly productive studies that have focused on specific molecules (e.g., hormones) and their specific immediate interacting partners. However, biomolecules are not isolated particles, but instead they are elements of highly integrated interaction networks, and specific interactions among them drive virtually all cellular functions and underlie phenotypic complexity and diversity. Many hormones, and their specific receptors and other interacting proteins, are known to be evolutionarily related, which raises intriguing questions concerning how specificity originated within these systems. Our previous studies have illustrated that biochemical entities are developmentally and evolutionarily fluid, with capabilities to be altered both in composition and behavior. Gene birth and death are widespread phenomena in genome evolution and accounts for the great diversity of gene families involved in endocrinology. While concordance of evolutionary histories both in pattern and process of hormones, receptors and interacting proteins might be expected for integrated systems, studies have shown that the evolutionary history of receptors need not mirror that of their ligands. Simultaneous emergence, or loss, of multiple interacting partners by multiple gene duplication or gene loss is unlikely in evolution. Gene duplication is essential in the development of complex endocrinology. It is creative in producing elements that allow evolutionary tinkering and thus plays a major role in gene co-option (i.e., recruitment for novel functions) facilitating the evolution of greater biological complexity. Alternatively, if an interacting partner is lost, the retained partner may either be subsequently lost or, more interestingly, serve as raw material in evolution and become recruited into a new interaction yielding a new function. Thus, a stepwise process of elaboration through mutation and optimization ensues, adapting genes (and their encoded proteins) into the physiology of an organism. Here we review several recent advances in our understanding of the evolution of hormone signalling pathways that illustrate the power of an evolutionary perspective. Among our
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".