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Record W2938601286 · doi:10.1101/602896

Impact of physico-chemical parameters on the denitrification and the co-occurrence of <i>Methylophaga nitratireducenticrescens</i> and <i>Hyphomicrobium nitrativorans</i> in a methanol-fed marine denitrifying biofilm

2019· preprint· en· W2938601286 on OpenAlexaff
Geneviève Payette, Valérie Geoffroy, Christine Martineau, Richard Villemur

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsArmand Frappier MuseumInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsDenitrifying bacteriaDenitrificationBiofilmEffluentAnoxic watersChemistryNitrateEnvironmental chemistryAerobic denitrificationNitriteMethanolMicroorganismEnvironmental engineeringBacteriaBiologyNitrogenEnvironmental scienceOrganic chemistry

Abstract

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Abstract Background The biofilm of a continuous, methanol-fed, fluidized denitrification system that treated a marine effluent is composed of multi-species microorganisms, among which Hyphomicrobium nitrativorans NL23 and Methylophaga nitratireducenticrescens JAM1 are the principal bacteria involved in the denitrifying activities. This biofilm can be cultured at laboratory-scale under batch mode conditions without losing the denitrifying activities. Here, we report the capacity of the denitrifying biofilm to sustain changes to specific physico-chemical parameters, and the impact of these changes on the denitrification performance and the co-occurrence of H. nitrativorans and M. nitratireducenticrescens . Methods The original biofilm (OB) taken from the denitrification system was acclimated to an artificial seawater (ASW) medium under anoxic conditions to generate the Reference biofilm cultures (300 mg-NO 3 − -N/L, 23°C). In the first set of assays, the Reference biofilm cultures were subjected to short exposures (1-3 days) of a range of NaCl, methanol, nitrate (NO 3 − ) and nitrite (NO 2 − ) concentrations, and to different pHs and temperatures. In the second set of assays, the OB was acclimated in an ASW-modified medium for five weeks i) to a range of NaCl concentrations (0% to 8%), ii) to four combinations of NO 3 − concentrations and temperatures, iii) to NO 2 − , and iv) under oxic conditions. Finally, the OB was acclimated to the commercial Instant Ocean (IO) medium. The growth of the biofilm and the dynamics of NO 3 − and NO 2 − were determined. The levels of M. nitratireducenticrescens and H. nitrativorans were measured by qPCR in these cultures. Results The denitrifying capacities of the OB was preserved in the Reference biofilm cultures. In these cultures, however, H. nitrativorans NL23 decreased by three orders of magnitude in concentration with the occurrence of the same magnitude of a new denitrifying bacterial strain ( M. nitratireducenticrescens GP59). Results from the first set of assays showed that the Reference biofilm cultures can sustain denitrifying activities in most of the tested conditions. Inhibition occurred when these biofilm cultures were exposed at pH 10 or with 1.5% methanol. Results from the second set of assays showed the persistence of H. nitrativorans NL23 in the biofilm cultures acclimated to low NaCl concentrations (0% to 1.0%). Poor biofilm development occurred in biofilm cultures acclimated to 5% and 8% NaCl. Finally, high proportion of H. nitrativorans NL23 was found in the IO biofilm cultures. Conclusions These results confirm the plasticity of the marine methylotrophic denitrifying biofilm in adapting to different conditions. The NaCl concentration is a crucial factor in the dynamics of H. nitrativorans NL23, for which growth was impaired above 1% NaCl in the ASW-based biofilm cultures in favor of M. nitratireducenticrescens GP59. This study contributes to the understanding on the population dynamics of co-occurring bacteria performing denitrifying activities in biofilm under seawater environment. This could benefit in the development of optimal denitrifying bioprocess under marine conditions.

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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 score0.003
Threshold uncertainty score0.006

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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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Citations1
Published2019
Admission routes1
Has abstractyes

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