Antimicrobial-specific response from resistance gene carriers studied in a natural, highly diverse microbiome

Abstract

Background Antimicrobial resistance (AMR) is a major threat to public health. Microorganisms equipped with AMR genes are suggested to have partially emerged from natural habitats; however, this hypothesis remains inconclusive so far. To understand the consequences of the introduction of exogenic antimicrobials into natural environments, we exposed lichen thalli of Peltigera polydactylon, which represent defined, highly diverse miniature ecosystems, to clinical (colistin, tetracycline), and non-clinical (glyphosate, alkylpyrazine) antimicrobials. We studied microbiome responses by analysing DNA- and RNA-based amplicon libraries and metagenomic datasets. Results The analyzed samples consisted of the thallus-forming fungus that is associated with cyanobacteria as well as other diverse and abundant bacterial communities (up to 108 16S rRNA gene copies ng-1 DNA) dominated by Alphaproteobacteria and Bacteroidetes. Moreover, the natural resistome of this meta-community encompassed 728 AMR genes spanning 30 antimicrobial classes. Following 10 days of exposure to the selected antimicrobials at four different concentrations (full therapeutic dosage and a gradient of sub-therapeutic dosages), we observed statistically significant, antimicrobial-specific shifts in the structure and function but not in bacterial abundances within the microbiota. We observed a relatively lower response after the exposure to the non-clinical compared to the clinical antimicrobial compounds. Furthermore, we observed specific bacterial responders, e.g., Pseudomonas and Burkholderia to clinical antimicrobials. Interestingly, the main positive responders naturally occur in low proportions in the lichen holobiont. Moreover, metagenomic recovery of the responders’ genomes suggested that they are all naturally equipped with specific genetic repertoires that allow them to thrive and bloom when exposed to antimicrobials. Of the responders, Sphingomonas, Pseudomonas, and Methylobacterium showed the highest potential. Conclusions Antimicrobial exposure resulted in a microbial dysbiosis due to a bloom of naturally low abundant taxa (positive responders) with specific AMR features. Overall, this study provides mechanistic insights into community-level responses of a native microbiota to antimicrobials and suggests novel strategies for AMR prediction and management.

Metagenome-assembled genomes indicate that antimicrobial resistance genes are highly prevalent among urban bacteria and multidrug and glycopeptide resistances are ubiquitous in most taxa

“…We identified the presence of ARGs in each MAG using DeepARG-LS (Arango-Argoty et al, 2018). ARGs with 6 https://github.com/mdsufz/UrbanARGs equal or higher than 80% probability, an e-value lower than 1 × 10 −10 , and percent identity of 35% or higher (Wicaksono et al, 2021) were considered present. Because we are working with MAGs that largely reflect the uncultured, and thus less known, proportion of urban prokaryotes, it is fitting to allow for the prediction of novel ARGs within our data.…”

“…We first translated the MAG genome sequences to amino acid sequences using the faTrans tool from KentUtils ( Kent, 2022 ). We defined ARGs as present in our MAGs when they met the following DeepARG-LS output criteria: equal or higher than 80% probability, an e-value lower than 1 × 10 −10 , and percent identity of 35% or higher, as done by Wicaksono et al in a recent study ( Wicaksono et al, 2021 ; Supplementary Table 4 ). We chose the 35% identity cutoff to utilize the novel ARG prediction power of the DeepARG-LS model.…”

Indirect Effects of the Herbicide Glyphosate on Plant, Animal and Human Health Through its Effects on Microbial Communities

“…Such resistance mechanisms may have led to the cross-resistance against antibiotics observed for E. coli, Salmonella sp. and other environmental bacteria (Kurenbach et al, 2015;Kurenbach et al, 2018;van Bruggen et al, 2018;Wicaksono et al, 2021). Other glyphosate resistance mechanisms have not been associated with antibiotic resistance (Pöppe et al, 2020).…”

“…Intestinal microbiomes assist in the bioconversion of nutrients and detoxification, determine host immunity, protect against pathogenic microorganisms, and promote health (Raymann and Moran, 2018;Rinninella et al, 2019;Scotti et al, 2017). Changes in the intestinal composition can lead to dysbiosis, characterized by an imbalance between beneficial and pathogenic microorganisms (Rinninella et al, 2019;Rueda-Ruzafa et al, 2019;Scotti et al, 2017;Wicaksono et al, 2021). A balanced gut microbiome does not only affect the functioning and health of the gastrointestinal tract by defending against pathogen invasion, but also interacts with the endocrine and nervous systems affecting the functioning and health of the whole host system (Clair É. et al, 2012;Ingaramo et al, 2020;Maddalon et al, 2021;Rinninella et al, 2019;Scotti et al, 2017).…”

Antibiotic-Induced Recruitment of Specific Algae-Associated Microbiome Enhances the Adaptability of Chlorella vulgaris to Antibiotic Stress and Incidence of Antibiotic Resistance

“…The total bacterial density, composition, and abundance of ARGs and genes encoding mobile genetic elements (MGEs) carried by bacteria were characterized by quantitative real-time PCR (qPCR). 37 In the qPCR, 29 primer pairs were used to amplify DNA sequences encoding 1 16S rRNA, 23 ARG, and 5 MGE genes, respectively. Specifically, these ARGs included tetracycline resistance genes (tetA, tetB, tetC, tetG, tetM, tetO, tetQ, tetW, and tetZ), quinolone resistance genes (aaC(6′)-Ib, gyrA, oqxAB, qepA, qnrA, qnrB, qnrC, qnrD, and qnrS), aminoglycoside resistance genes (aph(2′)-Id), β-lactam resistance genes (bla SHV ), macrolide resistance genes (ermC), chloramphenicol resistance genes (f loR), and sulfonamide resistance genes (sul2).…”