MétaCan
Menu
Back to cohort
Record W3125735784 · doi:10.3389/fmars.2020.596251

An Integrative Model of Carbon and Nitrogen Metabolism in a Common Deep-Sea Sponge (Geodia barretti)

2021· article· en· W3125735784 on OpenAlexaff
Anna de Kluijver, Martijn C. Bart, Dick van Oevelen, Jasper M. de Goeij, Sally P. Leys, Sandra R. Maier, Manuel Maldonado, Karline Soetaert, Sander Verbiest, Jack J. Middelburg

Bibliographic record

VenueFrontiers in Marine Science · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMarine Sponges and Natural Products
Canadian institutionsUniversity of Alberta
FundersExecutive Agency for Small and Medium-sized EnterprisesKoninklijk Nederlands Instituut voor Onderzoek der ZeeEuropean Commission
KeywordsDissolved organic carbonSpongeNitrogenAlgorithmChemistryBiologyEnvironmental chemistryComputer sciencePaleontologyOrganic chemistry

Abstract

fetched live from OpenAlex

Deep-sea sponges and their microbial symbionts transform various forms of carbon (C) and nitrogen (N) via several metabolic pathways, which, for a large part, are poorly quantified. Previous flux studies on the common deep-sea sponge Geodia barretti consistently revealed net consumption of dissolved organic carbon (DOC) and oxygen (O 2 ) and net release of nitrate ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="INEQ1"><mml:msubsup><mml:mtext>NO</mml:mtext><mml:mn>3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:math> ). Here we present a biogeochemical metabolic network model that, for the first time, quantifies C and N fluxes within the sponge holobiont in a consistent manner, including many poorly constrained metabolic conversions. Using two datasets covering a range of individual G. barretti sizes (10–3,500 ml), we found that the variability in metabolic rates partially resulted from body size as O 2 uptake allometrically scales with sponge volume. Our model analysis confirmed that dissolved organic matter (DOM), with an estimated C:N ratio of 7.7 ± 1.4, is the main energy source of G. barretti. DOM is primarily used for aerobic respiration, then for dissimilatory <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="INEQ2"><mml:msubsup><mml:mtext>NO</mml:mtext><mml:mn>3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:math> reduction to ammonium ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="INEQ3"><mml:mrow><mml:msubsup><mml:mtext>NH</mml:mtext><mml:mn>4</mml:mn><mml:mo>+</mml:mo></mml:msubsup><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math> (DNRA), and, lastly, for denitrification. Dissolved organic carbon (DOC) production efficiencies (production/assimilation) were estimated as 24 ± 8% (larger individuals) and 31 ± 9% (smaller individuals), so most DOC was respired to carbon dioxide (CO 2 ), which was released in a net ratio of 0.77–0.81 to O 2 consumption. Internally produced <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="INEQ4"><mml:msubsup><mml:mtext>NH</mml:mtext><mml:mn>4</mml:mn><mml:mo>+</mml:mo></mml:msubsup></mml:math> from cellular excretion and DNRA fueled nitrification. Nitrification-associated chemoautotrophic production contributed 5.1–6.7 ± 3.0% to total sponge production. While overall metabolic patterns were rather independent of sponge size, (volume-)specific rates were lower in larger sponges compared to smaller individuals. Specific biomass production rates were 0.16% day –1 in smaller compared to 0.067% day –1 in larger G. barretti as expected for slow-growing deep-sea organisms. Collectively, our approach shows that metabolic modeling of hard-to-reach, deep-water sponges can be used to predict community-based biogeochemical fluxes and sponge production that will facilitate further investigations on the functional integration and the ecological significance of sponge aggregations in deep-sea ecosystems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.236
Teacher spread0.231 · 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 teacher head, 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

Citations27
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Marine ScienceSame topicMarine Sponges and Natural ProductsFrench-language works237,207