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Record W2233626750 · doi:10.1242/jeb.135442

Gas movement through aquaporins is significant

2015· article· en· W2233626750 on OpenAlexaboutno aff
Kathryn Knight

Bibliographic record

VenueJournal of Experimental Biology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
Fundersnot available
KeywordsMembraneAquaporinZebrafishBespokeMembrane proteinChemistryNanotechnologyBiologyBiophysicsCell biologyBiochemistryMaterials scienceBusinessGene

Abstract

fetched live from OpenAlex

The movement of gases across biological structures is essential for life from the instant of conception to the last gasp. However, Katie Gilmour from the University of Ottawa, Canada, explains that there is much debate about the mechanisms by which gas molecules pass across membranes to move into and out of cells. Although gases can simply diffuse across membranes, certain membrane-embedded pore proteins – such as aquaporin water channels and Rhesus proteins – also allow gas molecules to pass through membranes. She says, ‘Arguing that membrane proteins are physiologically important for gas movement when membrane proteins are relatively limited (in comparison to overall membrane area) becomes challenging.’ So, in a bid to resolve the mystery, Gilmour and her colleagues turned to 4-day-old zebrafish larvae to find out just how significant aquaporins are in the transfer of gases across cell membranes.Gilmour explains that zebrafish larvae were great animals for her team to work with because it is possible to directly switch off the production of specific proteins and measure the impact on the amount of gas passing through cell membranes. However, she admits that working with the minute animals was extremely fiddly. ‘Measuring CO2 excretion in tiny aquatic animals requires that very small increases be measured in the CO2 concentration of the water in which the animals are held’, says Gilmour. But Mike Murphy, a talented engineer in the University of Ottawa's electrical workshop, eventually designed and built a bespoke CO2 analyser to allow Gilmour and her colleagues to make the sensitive measurements.Then, Krystle Talbot painstakingly injected a molecule specially designed to switch off production of aquaporin protein into newly fertilized zebrafish eggs and allowed them to develop for 4 days before measuring the amount of CO2 produced by the larvae. Amazingly, the larvae's CO2 excretion rate fell by 35%, despite consuming the same amount of oxygen as larvae with aquaporin proteins embedded in their cell membranes. The aquaporin proteins were contributing significantly to the movement of gas molecules across cell membranes.However, it was not clear whether the aquaporin proteins were involved in CO2 moving across red blood cell membranes or the membrane surrounding the larvae's yolk sac. So Talbot bathed the tiny animals in phenylhydrazine-water, to remove their red blood cells, and measured how much CO2 they produced. The larvae were unaffected, excreting as much CO2 as fish with red blood cells. However, when Talbot tested the CO2 production of larvae that lacked both red blood cells and aquaporin proteins, she found that it fell, so the aquaporin molecules embedded in the yolk sac membrane were responsible for CO2 excretion.Aquaporins have also been suggested to excrete toxic ammonia gas through cell membranes, so Talbot then measured ammonia excretion in larvae that did not produce aquaporin and in a second group of larvae that did not produce the Rhesus ammonia channel. Not surprisingly, the larvae lacking the Rhesus protein channel had significantly reduced ammonia excretion rates, but so too did the fish lacking aquaporin. And, when Talbot and Raymond Kwong investigated aquaporin gene expression and protein production in larvae that lacked the Rhesus protein when there were high levels of ammonia in the environment, they found that the larvae were mobilising more aquaporin. So, aquaporins could be working together with Rhesus proteins to excrete nitrogenous waste, in addition to helping the animals remove CO2 from their bodies.

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)0.001
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.056
GPT teacher head0.302
Teacher spread0.246 · 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
Published2015
Admission routes1
Has abstractyes

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