A multi-omics study on quantifying antimicrobial resistance in European freshwater lakes

Spänig, Sebastian; Eick, Lisa; Nuy, Julia K.; Beißer, Daniela; Ip, Margaret; Heider, Dominik; Boenigk, Jens

doi:10.1016/j.envint.2021.106821

Cited by 10 publications

(5 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The content of these documents included classical investigations about disease-causing organisms (e.g., Faleye et al, 2021 ; Lizasoain et al, 2021 ; Ahmed et al, 2021 ), illicit drug use (e.g., Bijlsma et al, 2021 ; Bade et al, 2021 ; Asicioglu et al, 2021 ) and pharmaceutical substances (e.g., Escolà Casas et al, 2021 ; Bodík et al, 2021 ; Yan et al, 2021 ). Investigations extended diversity to address antimicrobial-resistant bacteria ( Spänig et al, 2021 ; Kumar et al, 2021a , Kumar et al, 2021b ; Gumede et al, 2021 ), disease biomarker proteins ( Mohapatra et al, 2021 ), pesticide exposure ( Rousis et al, 2021 ; Vera-Herrera et al, 2021 ) and plasticizers ( Estévez-Danta et al, 2021a , Estévez-Danta et al, 2021b ), and consumption of sweeteners ( Li et al, 2021a , Li et al, 2021b ). Of these reports, 80 % dealt with disease-causing organisms identified in sewage ( Table 1 ) and 66 % with SARS-CoV-2 RNA (e.g., Hillary et al, 2021 ; Agrawal et al, 2021 ; Mota et al, 2021 ).…”

Section: State Of the Art Of Wastewater-based Epidemiology Researchmentioning

confidence: 99%

How has the COVID-19 pandemic impacted wastewater-based epidemiology?

Barcellos,

Barquilha,

Oliveira

et al. 2023

Science of The Total Environment

View full text Add to dashboard Cite

Section: State Of the Art Of Wastewater-based Epidemiology Researchmentioning

confidence: 99%

How has the COVID-19 pandemic impacted wastewater-based epidemiology?

Barcellos,

Barquilha,

Oliveira

et al. 2023

Science of The Total Environment

View full text Add to dashboard Cite

“…Meta-omics studies from different natural ecosystems specially aquatic environments such as ocean 23 , 24 , rivers 25 , lakes 26 and sea water 27 have recently detected ARGs and profiled the antibiotic resistome of these ecosystems. These studies reiterate that even natural environments that have not been exposed to high antibiotic concentrations could potentially be a reservoir of ARGs.…”

Section: Introductionmentioning

confidence: 99%

Genome-resolved insight into the reservoir of antibiotic resistance genes in aquatic microbial community

Goodarzi

Asad

Mehrshad

2022

Sci Rep

View full text Add to dashboard Cite

Aquatic microbial communities are an important reservoir of antibiotic resistance genes (ARGs). However, distribution and diversity of different ARG categories in environmental microbes with different ecological strategies is not yet well studied. Despite the potential exposure of the southern part of the Caspian Sea to the release of antibiotics, little is known about its natural resistome profile. We used a combination of Hidden Markov model (HMM), homology alignment and a deep learning approach for comprehensive screening of the diversity and distribution of ARGs in the Caspian Sea metagenomes at genome resolution. Detected ARGs were classified into five antibiotic resistance categories including prevention of access to target (44%), modification/protection of targets (30%), direct modification of antibiotics (22%), stress resistance (3%), and metal resistance (1%). The 102 detected ARG containing metagenome-assembled genomes of the Caspian Sea were dominated by representatives of Acidimicrobiia, Gammaproteobacteria, and Actinobacteria classes. Comparative analysis revealed that the highly abundant, oligotrophic, and genome streamlined representatives of taxa Acidimicrobiia and Actinobacteria modify the antibiotic target via mutation to develop antibiotic resistance rather than carrying extra resistance genes. Our results help with understanding how the encoded resistance categories of each genome are aligned with its ecological strategies.

show abstract

“…The copyright holder for this preprint this version posted February 9, 2022. ; https://doi.org/10.1101/2022.02.06.479282 doi: bioRxiv preprint ing alternative strategies is further underpinned by a recent study, which detected a certain baseline resistance in European freshwater lakes [2]. The study confirmed resistance specifically against four critical drug classes in human and veterinary health in freshwater, which is typically considered as a pathogen-free environment [2]. Moreover, already concerning levels of antibiotic resistance in Indian and Chinese lakes emphasize the requirement of alternative biocides [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…In the last decades, a rising number of bacterial species developed mechanisms to elude efficiency to widely used antibiotics [1]. The importance of developing and implementing alternative strategies is further underpinned by a recent study, which detected a certain baseline resistance in European freshwater lakes [2]. The study confirmed resistance specifically against four critical drug classes in human and veterinary health in freshwater, which is typically considered as a pathogen-free environment [2].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised encoding selection through ensemble pruning for biomedical classification

Spaenig

Michel

Heider

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever growing amount of annotated peptides, researchers employed artificial intelligence to circumvent the cumbersome, wet-lab-based identification and automate the detection of promising candidates. However, the prediction of a peptide's function is not limited to antimicrobial efficiency. To date, multiple studies successfully classified additional properties, e.g., antiviral or cell-penetrating effects. In this light, ensemble classifiers are employed to utilize the advantages of peptide encodings; hence, further improving the prediction. Although we recently presented a workflow to significantly diminish the initial encoding choice, an entire unsupervised encoding selection, considering various machine learning models, is still lacking. We developed a workflow, automatically selecting encodings and generating classifier ensembles by employing sophisticated pruning methods. We observed that the Pareto frontier pruning is a good method to create encoding ensembles for the datasets at hand. In addition, encodings combined with the Decision Tree classifier as the base model are often superior. However, our results also demonstrate that none of the ensemble building techniques is outstanding for all datasets. The workflow conducts multiple pruning methods to evaluate ensemble classifiers composed from a wide range of peptide encodings and base models. Consequently, researchers can use the workflow for unsupervised encoding selection and ensemble creation. Ultimately, the extensible workflow can be used as a plugin for the PEPTIDE REACToR, further establishing it as a versatile tool in the domain.

show abstract

A multi-omics study on quantifying antimicrobial resistance in European freshwater lakes

Abstract: Graphical abstract

Cited by 10 publications

References 55 publications

How has the COVID-19 pandemic impacted wastewater-based epidemiology?

How has the COVID-19 pandemic impacted wastewater-based epidemiology?

Genome-resolved insight into the reservoir of antibiotic resistance genes in aquatic microbial community

Unsupervised encoding selection through ensemble pruning for biomedical classification

Contact Info

Product

Resources

About