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Record W4413501856 · doi:10.1128/spectrum.00164-25

Concurrent stimulation of diflufenican biodegradation and changes in the active microbiome in gravel revealed by Total RNA

2025· article· en· W4413501856 on OpenAlexafffund
Lea Ellegaard‐Jensen, Pedro N. Carvalho, Muhammad Zohaib Anwar, Morten Dencker Schostag, Kai Bester, Carsten Suhr Jacobsen

Bibliographic record

VenueMicrobiology Spectrum · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsSimon Fraser University
FundersHorizon 2020 Framework ProgrammeNovo Nordisk FondenCanadian Institutes of Health ResearchAarhus Universitets ForskningsfondMiljøstyrelsenDanmarks Grundforskningsfond
KeywordsMicrobial population biologyBacteroidetesBiotransformationBiologyMicrobial biodegradationBiodegradationTranscriptomeMicrocosmChemistryGene expressionBacteriaGeneMicroorganismBiochemistryEcologyGenetics16S ribosomal RNAEnzyme

Abstract

fetched live from OpenAlex

ABSTRACT The use of slowly degraded pesticides poses a particular problem when these are applied to urban areas such as gravel paths. The urban gravel provides an environment very different from agricultural soils; i.e., it is both lower in carbon and microbial activity. We, therefore, endeavored to stimulate the degradation of the pesticide diflufenican added to urban gravel microcosms amended with dry alfalfa to increase microbial activity. In the present study, alfalfa addition significantly increased the formation of diflufenican’s primary metabolite, 2-[3-(trifluoromethyl)phenoxy]nicotinic acid (AE-B), indicating stimulated biotransformation. The concurrent changes of the active microbial communities within the gravel were explored using shotgun metatranscriptomic sequencing of ribosomal RNA and messenger RNA. Although bacterial taxa remained dominant (87.0%–98.5% relative abundance), the alfalfa treatment led to a 4–5-fold increase in eukaryotic groups, including fungi and microbial grazers. Several microbial taxa potentially involved in the degradation of complex carbon compounds and aromatic pollutants—including Bacteroidetes , Verrucomicrobia , Sordariomycetes , Mortierellales , Tremellales, Sphingopyxis , and Phenylobacterium —increased in relative abundance following alfalfa amendment. Functional gene profiling revealed elevated expression of genes related to microbial activity and biomass production. Genes with potential roles in the breakdown of complex carbon structures (e.g., xylanases/chitin deacetylases) and in the transformation of aromatic compounds (e.g., ring-cleaving dioxygenases) were revealed. We conclude that complex carbon amendments can enhance the microbial activity, promoting the biotransformation of diflufenican in urban gravel environments. These findings provide new insights into the interactions between microbial community dynamics, gene expression profiles, and pesticide biotransformation in non-agricultural matrices. IMPORTANCE Pesticides used on urban areas, e.g., gravel paths, are likely to have different effects and fates than when these are used on agricultural soils. Hence, studies into the degradation of pesticides applied to urban matrices are needed. We have previously shown that metabolites of the persistent pesticide diflufenican are even more persistent in urban soils, and it has also previously been shown that these metabolites leach from gravel surfaces. The reasons behind this are that the urban gravel provides an environment very different from agricultural soils; i.e., it is both lower in carbon and microbial activity. In the present study, we, therefore, endeavored to stimulate the degradation of the pesticide diflufenican added to urban gravel microcosms amended with dry alfalfa to increase microbial activity, concurrently studying the changes in the active microbiome by Total RNA-metatranscriptomics.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

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 head0.006
GPT teacher head0.223
Teacher spread0.218 · 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 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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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