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Record W4415117521 · doi:10.7717/peerj.20129

Modulation of carbon-to-nitrogen ratio shapes the microbial ecology in a methanol-fed recirculating marine denitrifying reactor

2025· article· en· W4415117521 on OpenAlexafffund
Livie Lestin, Richard Villemur

Bibliographic record

VenuePeerJ · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaInstitut national de la recherche scientifique
KeywordsDenitrificationDenitrifying bacteriaHeterotrophMicrobial population biologySeawaterBiofilmWastewaterAerobic denitrification

Abstract

fetched live from OpenAlex

Background Nitrate (NO3−) can accumulate in closed-circuit ecosystems to a toxic level. Adding heterotrophic denitrification process to the water treatment is a strategy to reduce this level. This type of process usually requires the addition of a carbon source. Carbon-to-nitrogen ratio (C/N) is a key parameter known to influence both the function and the activity of microbial communities in bioprocesses. Few studies have examined the influence of C/N on denitrification systems operated under methylotrophic and marine conditions. Here we assessed the influence of C/N (methanol and NO3−) on the performance of a laboratory-scale, recirculating denitrifying reactor operated under marine conditions. We monitored the evolution of the bacterial community in the biofilm to assess its stability during the operating conditions. Finally, the relative gene expression profiles of Methylophaga nitratireducenticrescens strain GP59, the main denitrifier in the denitrifying biofilm, were determined during the operating conditions and compared with those of GP59 planktonic pure cultures. Methodology A 500-mL methanol-fed recirculating denitrification reactor operated under marine conditions and colonized by a naturally occurring multispecies denitrifying biofilm was subjected to eight different C/N. We monitored several physico-chemical parameters (denitrifying activities, methanol consumption, CO2 production) throughout the operating conditions. The evolution of the bacterial community in the biofilm during these conditions was determined by 16S rRNA gene amplicon sequencing. Metatranscriptomes were derived from the biofilm to determine (1) the relative gene expression profiles of strain GP59, and (2) the functional diversity of the active microorganisms in the biofilm. Results Changes in C/N did not correlate with the denitrification dynamics (NO3− and NO2− reduction rates, NO2− and N2O dynamics), but did correlate with the methanol consumption rates, and the CO2 production rates. Throughout the operating conditions, nitrite and N2O appeared transiently, and ammonium was not observed. The bacterial community in the reactor increased in diversity with biofilm aging, especially among heterotrophic bacteria, at the expense of methylotrophic bacteria. The relative expression profiles of strain GP59 in the biofilm are distinct from those of planktonic pure cultures of strain GP59, and that the expression of several riboswitches and xoxF would be involved in these differences. Conclusions When the biofilm community is well established in the reactor, it can withstand changes in C/N with limited impact on the denitrification performance. The increase in the proportion of heterotrophs would allow the reactor to be more flexible regarding carbon sources. This knowledge can be useful for improving the efficiency of denitrification system treating close circuit systems such as marine recirculating aquaculture wastewater or seawater aquarium.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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