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Record W2916555536 · doi:10.1101/564823

Antibiotic resistome and microbial community structure during anaerobic co-digestion of food waste, paper and cardboard

2019· preprint· en· W2916555536 on OpenAlexafffund
Kärt Kanger, Nigel G. H. Guilford, HyunWoo Lee, Camilla Nesbø, Jaak Truu, Elizabeth A. Edwards

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of AlbertaAptose Biosciences (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCollege of Engineering, Michigan State UniversityMichigan State University
KeywordsResistomeAnaerobic digestionMetagenomicsMethanosaetaBiologyDigestateFood scienceFood wasteMicrobial population biologyMesophileMicrobiologyFirmicutesBiotechnologyAntibiotic resistanceBacteria16S ribosomal RNAAntibioticsIntegronEcology

Abstract

fetched live from OpenAlex

ABSTRACT Antimicrobial resistance is a globally recognized public health risk. High incidence of antibiotic resistant bacteria and antibiotic resistance genes (ARGs) in solid organic waste necessitates the development of effective treatment strategies. The objective of this study was to assess ARG diversity and abundance as well as the relationship between resistome and microbial community structure during anaerobic co-digestion (AD) of food waste, paper and cardboard. A lab-scale solid-state AD system consisting of six sequentially fed leach beds (each with a solids retention time of 42 days) and an upflow anaerobic sludge blanket (UASB) reactor was operated under mesophilic conditions continuously for 88 weeks to successfully treat municipal organic waste and produce biogas. A total of ten samples from digester feed and digestion products were collected for microbial community analysis including SSU rRNA gene sequencing, total community metagenome sequencing and quantitative PCR. Taxonomic analyses revealed that AD changed the taxonomic profile of the microbial community: digester feed was dominated by bacterial and eukaryotic taxa while anaerobic digestate possessed a large proportion of archaea mainly belonging to the methanogenic genus Methanosaeta . ARGs were identified in all samples with significantly higher richness and relative abundance per 16S rRNA gene in digester feed compared to digestion products. Multidrug resistance was the most abundant ARG type. AD was not able to completely remove ARGs as shown by ARGs detected in digestion products. Using metagenomic assembly and binning we detected potential bacterial hosts of ARGs in digester feed, that included Erwinia, Bifidobacteriaceae, Lactococcus lactis and Lactobacillus . IMPORTANCE Solid organic waste is a significant source of antibiotic resistance genes (ARGs) (1) and effective treatment strategies are urgently required to limit the spread of antimicrobial resistance. Here we studied the antibiotic resistome and microbial community structure within an anaerobic digester treating a mixture of food waste, paper and cardboard. We observed a significant shift in microbial community composition and a reduction in ARG diversity and abundance after 6 weeks of digestion. We identified the host organisms of some of the ARGs including potentially pathogenic as well as non-pathogenic bacteria, and we detected mobile genetic elements required for horizontal gene transfer. Our results indicate that the process of sequential solid-state anaerobic digestion of food waste, paper and cardboard tested herein provides a significant reduction in the relative abundance of ARGs per 16S rRNA gene.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.221
Teacher spread0.208 · 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".

Quick stats

Citations2
Published2019
Admission routes2
Has abstractyes

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