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Record W2333948993 · doi:10.1242/jeb.064238

FRUIT FLIES ON ICE

2012· article· en· W2333948993 on OpenAlexaff
Katie E. Marshall

Bibliographic record

VenueJournal of Experimental Biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsWestern University
Fundersnot available
KeywordsDrosophila melanogasterMelanogasterDiapauseLarvaBiologyVitrificationOverwinteringInsectZoologyEcologyGeneticsPhysics

Abstract

fetched live from OpenAlex

The ability to survive freezing comes naturally to a select group of insects. These cold-adapted insect species live in areas where they might experience sub-zero temperatures for at least some of the year. But Drosophila melanogaster, the favourite fly of researchers from several life sciences disciplines, is better known for its preference for the warmth of human kitchens and is injured by cold – even at temperatures above freezing. Scientists would love to know how to successfully put these flies into suspended animation using cryopreservation to maintain their valuable stocks of laboratory-modified D. melanogaster lines. In recent work published in Proceedings of the National Academy of Sciences, Vladimir Koštál and colleagues from the Czech Republic show that with a few simple tricks picked up from a freeze-tolerant cousin, it is possible to convert the chill-susceptible D. melanogaster into a fly that can survive freezing.Earlier work by Koštál and his colleagues showed that Chymomyza costata, a drosophilid fly closely related to D. melanogaster, has two requirements to survive freezing: (1) it must be in developmental arrest (called diapause) during an overwintering stage, and (2) it must accumulate large quantities of the free amino acid proline. The authors thought a similar protocol might work for D. melanogaster. First, the team reared larvae at either room temperature or a relatively low temperature for D. melanogaster (15°C) until they reached the final larval stage. Then, they subjected the larvae to temperatures that fluctuated between 6°C and 11°C for 3 days to induce a type of diapause. In addition, some of the insects were fed diets rich in known cryoprotectants: glycerol, proline or trehalose. Finally, the team slowly cooled the flies to –5°C and held them there for over an hour before allowing the insects to resume development.The researchers found that feeding the larvae diets rich in cryoprotectants or subjecting them to fluctuating temperatures both increased the larvae's survival of freezing, but only for a short time after the stress. However, the combination of the proline-rich diet in particular with the fluctuating temperatures had a synergistic effect, producing larvae with almost a 10% chance of surviving to reproduce successfully after being frozen at –5°C for over an hour – which is long enough to convert half of their body water to ice.To investigate how these treatments protected the diapausing larvae from freezing to death, the authors measured the concentration of several of the larvae's metabolites after consuming their cryoprotectant-supplemented diet, using mass spectrometry. They found that the larvae fed on the supplemented diets all accumulated extra cryoprotectant, although the proline-supplemented diet had the largest effect on cryoprotectant concentration. The authors thought that perhaps the larvae do not control their proline levels as tightly as they do other metabolites, which might have contributed to the success of proline in producing freeze tolerance.While Koštál and colleagues plan further studies into the mechanisms of proline's effects, this first report of inducing freeze tolerance in a tropical insect like D. melanogaster contributes to the elucidation of the mechanisms of natural freeze tolerance, and may lead to an end of the labour-intensive work of maintaining laboratory fly stocks.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0320.035

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.

Opus teacher head0.018
GPT teacher head0.311
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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