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Dietary Creatine Supplementation Reduced the Proportion of Dietary Arginine Directed toward Guanidinoacetic Acid (GAA) Synthesis and Reduced the Demand for Methionine‐Derived Methyl Groups, but Did Not Enhance Whole Body Protein Synthesis in Neonatal Piglets

2016· article· en· W2514908337 on OpenAlexafffund
O. Chandani Dinesh, Thillayampalam Kankayaliyan, M. Rademacher, Christopher Tomlinson, Robert F. Bertolo, Janet A. Brunton

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCreatineMethionineArginineChemistryAmino acidBiochemistryInternal medicineEndocrinologyFood scienceMedicine

Abstract

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Our previous work demonstrated that dietary arginine (Arg) intake influenced tissue creatine concentrations in neonatal piglets fed creatine‐free diets. Both Arg and methionine (Met) are required for creatine biosynthesis, as L‐arginine:glycine amidinotransferase (AGAT) converts Arg to guanidinoacetic acid (GAA),which is subsequently methylated by guanidinoacetate methyltransferase (GAMT) to form creatine, with Met as the methyl donor. In this study, we manipulated dietary Arg, Met, GAA and creatine supply to quantify the partitioning of amino acids towards GAA and creatine synthesis. Piglets (9 – 11 d old, N =35) were fed one of five elemental diets: 1) low Arg and low Met (Low Arg&Met), 2) low Arg and Met plus GAA, 3) low Arg and Met plus creatine 4) high Arg and high Met (High Arg&Met) or 5) low Arg with high Met plus GAA.On d 6, piglets underwent a primed, constant infusion of stable isotope tracers of Arg, GAA and creatine to measure Arg partitioning, and tracers of phenylalanine and tyrosine to measure whole body protein synthesis. On d 7, a second infusion of 3 H‐methyl‐methionine was conducted to trace the partitioning of methyl groups to creatine synthesis. GAA synthesis was limited by Arg and/or Met since piglets fed the Low Arg&Met diet had less Arg conversion to GAA compared to the High Arg&Met group (P<0.001). Renal AGAT activity in the Low Arg&Met was significantly higher than all other groups (P<0.01), suggesting that the lower conversion rate was due to inadequate substrate availability and not due to lack of enzyme activity. The inclusion of GAA with low Arg and Met resulted in significantly lower Arg partitioning to GAA (P<0.01) and to creatine (P<0.001). Liver GAMT activity was similar across diet groups, except when GAA was supplied with high Met (P<0.01), suggesting GAMT was induced to accommodate creatine synthesis only when Met was abundant. The addition of creatine to the Low Arg&Met diet resulted in higher kidney, liver and plasma creatine concentrations (P < 0.001), but these values were not different from those concentrations in the piglets fed High Arg&Met. The addition of creatine also resulted in a smaller proportion of dietary Arg directed towards GAA synthesis (63 versus 91%, respectively, P<0.001), and a lower incorporation of methionine‐derived methyl groups into creatine (P<0.05) compared to all other diet groups. Although supplementing GAA or creatine appeared to spare Arg and Met, whole body protein synthesis or breakdown were not different. When Arg and Met availabilities were limited, creatine supplementation led to a reduction in the demand for these amino acids to satisfy GAA and creatine synthesis. Consideration of dietary creatine availability is essential when determining Arg and Met requirements of the neonate. Support or Funding Information (NSERC and Evonik)

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.265
Teacher spread0.247 · 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
Published2016
Admission routes2
Has abstractyes

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