Bibliographic record
Abstract
Winter is generally not the best time of year for organisms. Temperatures are low and food is scarce, and for those species that can't fly south for the winter, surviving is a matter of hunkering down and making the best of a bad lot. But some individuals are lucky enough to carry different versions of the same gene (alleles), which make them better at surviving the demands of winter than others; particularly those animals endowed with genes that are able to reduce their metabolic demands, allowing them to survive winter with less food. Not surprisingly, these extra-tough creatures are often those that live at higher latitudes where winters tend to be harsher and longer. Now, a team of researchers from Stony Brook University and the University of Pennsylvania, USA, led by Rodrigo Cogni, have shown that the genetic changes associated with survival at higher latitudes in the fruit fly Drosophila melanogaster are also associated with seasonal shifts in metabolism.Drosophila melanogaster is a short-lived fly that can produce a new generation every few weeks in warm weather, so the researchers were curious whether there could be adaptive changes in the frequency at which these tougher alleles are found over the span of seasons, as well as in geographic space. Previously, they had shown that the version of a gene known as couch potato, which is associated with the seasonal shutdown of metabolism, was found more commonly at higher latitudes in spring than at lower latitudes in summer. In their most recent study, they broadened their scope to genes responsible for the major branching points in metabolism to see whether the way that versions of couch potato varied over time and space could be generalized across the major metabolic pathways.The team began by focusing on single nucleotide polymorphisms (SNPs) in 46 genes that code for central metabolic enzymes identified from the 37 different genome sequences for Drosophila melanogaster published in the genetic database Flybase. To study populations along a latitudinal gradient, they collected flies from 20 different populations in eastern North America, from the city of Sudbury in Northern Ontario through to southern Florida. They also collected flies from a single orchard in the southern part of the range several times to study the population over the course of a year.From these collections, Cogni and his team sequenced copies of each of the genes that they were interested in, then estimated how often different base pairs were found at each SNP in each population, then correlated the frequency of each allele to latitude and time of year of collection. They found that 31 out of 46 genes had allele frequencies that changed significantly with latitude, but only two had frequencies that changed with time of year. But those genes whose allele frequencies tended to be highly correlated with latitude also tended to have allele frequencies that were highly correlated with the time of year. And this pattern became stronger when the team looked only at those SNPs that were correlated with latitude, which the authors suggest means that these areas are under particular selection for adaptation to cold weather whether due to seasonal shifts or latitudinal.For a fly out in the cold the challenges are best handled by a well-adapted set of genes. And it turns out that the exact same pathways that adapt flies to a northern climate also help with seasonal shifts too.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".