The genome of the water strider <i>Gerris buenoi</i> reveals expansions of gene repertoires associated with adaptations to life on the water
Bibliographic record
Abstract
Abstract The semi-aquatic bugs conquered water surfaces worldwide and occupy ponds, streams, lakes, mangroves, and even open oceans. As such, they inspired a range of scientific studies from ecology and evolution to developmental genetics and hydrodynamics of fluid locomotion. However, the lack of a representative water strider genome hinders thorough investigations of the mechanisms underlying the processes of adaptation and diversification in this group. Here we report the sequencing and manual annotation of the Gerris buenoi (G. buenoi) genome, the first water strider genome to be sequenced so far. G. buenoi genome is about 1 000Mb and the sequencing effort recovered 20 949 predicted protein-coding genes. Manual annotation uncovered a number of local (tandem and proximal) gene duplications and expansions of gene families known for their importance in a variety of processes associated with morphological and physiological adaptations to water surface lifestyle. These expansions affect key processes such as growth, vision, desiccation resistance, detoxification, olfaction and epigenetic components. Strikingly, the G. buenoi genome contains three Insulin Receptors, a unique case among metazoans, suggesting key changes in the rewiring and function of the insulin pathway. Other genomic changes include wavelength sensitivity shifts in opsin proteins likely in association with the requirements of vision in water habitats. Our findings suggest that local gene duplications might have had an important role during the evolution of water striders. These findings along with the G. buenoi genome open exciting research opportunities to understand adaptation and genome evolution of this unique hemimetabolous insect.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".