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Unique Aspects of Copper Binding Components in the Blood Plasma of Dogs

2019· article· en· W3174296189 on OpenAlexaboutno aff
Yasmine H. Alam, Kaitlynne Kim, Stephen Flynn, Hille Fieten, Scott Weldy, Maria C. Linder

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryCeruloplasminBlood proteinsFast protein liquid chromatographySize-exclusion chromatographyHistidineCopperAlbuminGlycoproteinBlood plasmaChromatographyBiochemistryHigh-performance liquid chromatographyAmino acidEnzyme

Abstract

fetched live from OpenAlex

It is well known that dogs suffer from at least two unusual traits related to Cu metabolism, which are (a) an inability of their serum albumin to bind Cu tightly, due to no histidine in the N terminal binding site; and (b) 10–20‐times higher liver Cu concentrations accompanied by a tendency to develop Cu overload.[1,2] We have identified and investigated two additional peculiarities in dogs related to components in their blood plasma. One has to do with ceruloplasmin (Cp), a 132 kDa glycoprotein, which has functions ranging from delivery of Cu to cells and scavenging reactive oxygen species, to oxidation of Fe(II) and various amines. It is also the main Cu binding component of the blood plasma. Although liver Cu concentrations are extremely high compared to other species, total Cu levels in plasma are normally much lower than those in human and rats (200–400 ng/ml versus about 1200). We found that when the plasma of Labrador retrievers was fractionated in large pore size exclusion chromatography (SEC), the main Cu peak eluted much earlier than is the case with plasma from other mammals. In Superdex 200 FPLC equilibrated with 20 mM phosphate, pH 7, the main Cu peak eluted near the void volume (Mr ~ 700 kDa) as compared to that of humans mice, rats and pigs (Mr~150 kDa). The same was the case for plasma from other dog breeds. Western blotting determined that Cp protein eluted in parallel with the main Cu peak and was of normal size in SDS‐PAGE. Enzyme activities associated with Cp (ferroxidase and p‐phenylenediamine oxidase) also eluted in parallel. This indicated that canine Cp was either bound to some other protein(s) or that it was aggregating. Lactoferrin, which has been reported to bind to Cp in milk, was not detected. Canine plasma proteins were further fractionated on Sephacryl S300, which gives a better separation of large components. Stained SDS‐PAGE gels of fractions from such columns did not show any bands that eluted in parallel with those of Cp. This is consistent with the concept that Cp was not binding another protein but aggregating. To further examine that possibility, fractionation was carried out in buffers of higher ionic strength. Isotonic phosphate‐buffered saline did not alter the elution of canine Cp in Superdex 200. However with 300 mM phosphate (pH 6.8), canine Cp eluted in the same way as the Cp of other mammals, indicating it had dissociated. The other canine peculiarity investigated relates to the tendency towards Cu toxicosis. Plasma from Labrador retrievers with mutations in the Cu transporter ATP7B and high liver copper concentrations had markedly increased levels of a small Cu carrier in their plasma and urine compared with the wild type, as is the case with Wilson disease model mice.[3] Our results indicate that increased levels of a small Cu carrier may be helpful in diagnosis of canine Cu overload, and suggest that canine Cp is circulating as a multimeric protein. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.289
Teacher spread0.256 · 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 designObservational
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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