Mechanisms underlying phenotypic plasticity in response to environmental change
Bibliographic record
Abstract
Understanding how human activity impacts natural systems is crucial for maintaining ecosystem health and the services these provide for societies. Phenotypic plasticity - the regulated expression of different phenotypes by a single genotype - is the most effective response to increase resistance or resilience of phenotypes to rapidly changing environments. Here, we review the mechanisms that underlie phenotypic plasticity in animals. Understanding the regulatory mechanisms is important because these determine the time course of establishment and persistence of alternative phenotypes. We propose that regulation of trans- and intergenerational plasticity, developmental plasticity and reversible acclimation involves (i) environmental information acquisition, (ii) signal integration, and (iii) translation of environmental information to alter phenotypes. We provide a high-level overview of each of these stages with the aim of summarising current knowledge and making it accessible to a broader audience who are not necessarily expert in neuroendocrine and molecular biology. Information acquisition occurs primarily by sensors that transduce environmental information (e.g. temperature, light, chemicals, etc.) to the central nervous system. An exception is AMP-activated protein kinase (AMPK) that senses cellular energy levels and interacts locally and with neuroendocrine systems to adjust anabolic and catabolic metabolism. Signal integration is achieved primarily by neural and endocrine mechanisms. The major players are the autonomic nervous system, the hypothalamus-pituitary-adrenal/interrenal (HPA/I), the hypothalamus-pituitary-thyroid (HPT), and the hypothalamus-pituitary-somatotrophic (HPS) axes, which receive environmental information from the brain and transmit it via hormone signalling. Phenotypic effects of these major axes can be directly to the target tissues, or via epigenetically modified gene expression programs. DNA methylation, histone modifications, and microRNAs are the principal epigenetic processes, of which the first two are regulated by neuroendocrine signalling. Importantly, all of these processes (AMPK, neuroendocrine, epigenetic) interact with each other so that regulation occurs in a network-like manner rather than by individual regulators alone. Nonetheless, an appreciation of individual mechanisms is an essential starting point that can guide future research into more complex interactions to advance understanding of the evolution and ecological importance of plasticity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".