Effects of cold-acclimation on gene expression in Fall field cricket (Gryllus pennsylvanicus) ionoregulatory tissues
Bibliographic record
Abstract
Background: Cold tolerance is a key determinant of temperate insect distribution and performance. Chill-susceptible insects lose ion and water homeostasis during cold exposure, but prior cold acclimation improves both cold tolerance and defense of homeostasis. The mechanisms underlying these processes are mostly unknown; cold acclimation is thought to enhance ion transport in the cold and/or prevent leak of water and ions. To identify candidate mechanisms of cold tolerance plasticity we generated transcriptomes of ionoregulatory tissues (hindgut and Malpighian tubules) from Gryllus pennsylvanicus crickets and compared gene expression in warm-and cold-acclimated individuals. Results: We assembled a G. pennsylvanicus transcriptome de novo from 286 million 50-bp reads, yielding 70,037 contigs (~44% of which had putative BLAST identities). We compared the transcriptomes of warmand cold-acclimated hindguts and Malpighian tubules. Cold acclimation led to a 2-fold change in the expression of 1493 hindgut genes (733 downregulated, 760 upregulated) and 2008 Malpighian tubule genes (1009 downregulated, 999 upregulated). Cold-acclimated crickets had altered expression of genes putatively associated with ion and water balance, including: a downregulation of V-ATPase and carbonic anhydrase in the Malpighian tubules and an upregulation of Na + -K + ATPase in the hindgut. We also observed acclimationrelated shifts in the expression of cytoskeletal genes in the hindgut, including actin and actin-anchoring/stabilizing proteins, tubulin, -actinin, and genes involved in adherens junctions organization. In both tissues, cold acclimation led to differential expression of genes encoding cytochrome P450s, glutathione-S-transferases, apoptosis factors, DNA repair, and heat shock proteins. Conclusions: This is the first G. pennsylvanicus transcriptome, and our tissue-specific approach yielded new candidate mechanisms of cold tolerance plasticity. Cold acclimation may reduce loss of hemolymph volume in the cold by 1) decreasing primary urine production via reduced expression of carbonic anhydrase and V-ATPase in the Malpighian tubules and 2) by increasing Na + (and therefore water) reabsorption across the hindgut via increase in Na + -K + ATPase expression. Cold acclimation may reduce chilling injury by remodeling and stabilizing the hindgut epithelial cytoskeleton and cell-to-cell junctions, and by increasing the expression of genes involved in DNA repair, detoxification, and protein chaperones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".