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MotivationWastewater treatment plants (WWTPs) harbor a dense and diverse microbial community. They constantly receive antimicrobial residues and resistant strains, and therefore provide conditions for horizontal gene transfer (HGT) of antimicrobial resistance (AMR) determinants. This facilitates the transmission of clinically important genes between, e.g. enteric and environmental bacteria, and vice versa. Despite the clinical importance, tools for predicting HGT remain underdeveloped.ResultsIn this study, we examined to which extent water cycle microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e. the ability of cells to receive a plasmid through conjugation, based on data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, representing the range of hosts a plasmid is able to transfer to, for three broad host-range resistance IncP plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.49 for pB10 [95% confidence interval (CI): 0.44–0.55], 0.43 for pKJK5 (0.95% CI: 0.41–0.49), and 0.53 for RP4 (0.95% CI: 0.48–0.57) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite the broad host-range nature of these plasmids. Our results provide a framework that contributes to the assessment of the risk of AMR pollution in wastewater systems.Availability and implementationThe predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm. MOTIVATION: Wastewater treatment plants (WWTPs) harbor a dense and diverse microbial community. They constantly receive antimicrobial residues and resistant strains, and therefore provide conditions for horizontal gene transfer (HGT) of antimicrobial resistance (AMR) determinants. This facilitates the transmission of clinically important genes between, e.g. enteric and environmental bacteria, and vice versa. Despite the clinical importance, tools for predicting HGT remain underdeveloped. RESULTS: In this study, we examined to which extent water cycle microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e. the ability of cells to receive a plasmid through conjugation, based on data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, representing the range of hosts a plasmid is able to transfer to, for three broad host-range resistance IncP plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.49 for pB10 [95% confidence interval (CI): 0.44-0.55], 0.43 for pKJK5 (0.95% CI: 0.41-0.49), and 0.53 for RP4 (0.95% CI: 0.48-0.57) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite the broad host-range nature of these plasmids. Our results provide a framework that contributes to the assessment of the risk of AMR pollution in wastewater systems. AVAILABILITY AND IMPLEMENTATION: The predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm.

Poster presentado en: 43rd Annual Meeting of the Spanish Society of Biochemistry and Molecular Biology SEBBM Barcelona, 19-22 Julio (2021), This study was supported by the Spanish Ministry of Science and Innovation (projects CTM2013-44271-R and CTM2017-86504-R) and European FEDER funds

Poster presentado en: XXVIII Congreso Sociedad Española de Microbiología SEM. On line, 28 junio - 2 julio (2021), Financing: Financiado por el Ministerio de Economía y Competitividad, la AEI y fondos FEDER (CTM2013-44271-R y CTM2017-86504-R AEI/FEDER, UE).

B. subtilis is a soil dwelling bacterium with a diverse social life that includes quorum sensing-regulated interactions 1 . These interactions result in bacterial equivalent of sex, and subsequently have a profound influence on bacterial evolution 2 . Sexual isolation in B. subtilis predicts for more frequent uptake of DNA isolated from closely related strains 3–5 , but DNA exchange between two interacting B. subtilis strains has never been addressed previously. Recently we discovered kin discrimination among highly related strains of B. subtilis , where less related strains showed antagonistic behaviour towards each other in the form of killing 6 . Here we show that antagonistic interactions between two less closely related B. subtilis strains result in increased recombination, which is in contrast to current dogma. We demonstrate that the induction of competence between non-kin strains is responsible for the observed elevated DNA uptake, which, through increased genetic variation, can increase the rate of adaptation as demonstrated here by the successful exploitation of a novel adaptive zone by a recombinant strain. Our results demonstrate an important evolutionary mechanism of ‘’promiscuous but safe sex’’: a type of bacterial cell-contact dependent DNA exchange that could promote diversification of conspecifics and exclude non-specific and potentially risky DNA of other species. Our findings could help understand the vast diversity of B. subtilis species at the genomic level despite existing mechanisms limiting less-related DNA integration during transformation. It is possible that this (or similar) mechanism could be accountable for the diversification of many other bacterial species capable of natural transformation.

Figure S1. Variation of weight of the sampled fish before, during and after exposure to florfenicol. Figure S2. Coverage/number of raw reads (A) and Nonpareil index diversity (B) from the bacterial community of Piaractus mesopotamicus before during and after antibiotic exposure. The index indicates the complexity of the bacterial community in terms of “Sequencing space”. Higher values indicate higher diversity. Figure S3. Effect of florfenicol on the relative abundance of the families (A) and genus (B) of the bacterial community of Piaractus mesopotamicus before, during, and after antibiotic exposure. Figure S4. Taxonomic (A), functional (B), and ARGs (C) structure of the gut bacterial community of Piaractus mesopotamicu before (day 0) and after antibiotic exposure (days 13, 18, 26, 34). Figure S5. Effect of the antibiotic florfenicol on the relative abundance of ARGs (A) and MGEs (B) of the gut bacterial community of Piaractus mesopotamicus before, during and after antibiotic treatment. Figure S6. Fold changes of different drug classes after the antibiotic treatment. Figure S7. Antibiotic resistance genes from plasmid origins. Figure S8. Correlation of total ribosomal protein L1 (A) and L12 (B) and the same genes co-occurring with MGEs genes before, during and after antibiotic exposure. Figure S9. The 30 Most abundant ARGs flanked by MGEs in the gut of Piaractus mesopotamicus before, during, and after antibiotic exposure. Figure S10. The eight most abundant genus (contigs) harboring ARGs from gut samples of Piaractus mesopotamicus before, during and after the antibiotic exposure. Table S1. Bacterial functional shift in the gut of P. mesopotamicus before, during, and after antibiotic exposure. (DOC 5958 kb)

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International audience; Soil is a large reservoir of microbial diversity and the majority of antimicrobial compounds used today in human and veterinary health care have been isolated from soil microorganisms. The Darwinian hypothesis of an 'arms-shields race' between antibiotic producers and resistant strains is often cited to explain antibiotic resistance gene determinants (ARGD) origins and diversity. ARGD abundance and antibiotic molecule exposure are, however, not systematically linked, and many other factors can contribute to resistance gene emergence, selection and dissemination in the environment. Soil is a heterogeneous habitat and represents a broad spectrum of different ecological niches. Soil harbours a large genetic diversity at small spatial scale, favouring exchange of genetic materials by means of horizontal gene transfer (HGT) that will contribute to ARGD dissemination between bacteria and eventually acquisition by pathogen genomes, therefore threatening antibiotic therapies. Our current knowledge on the extent of the soil resistome abundance and diversity has been greatly enhanced since the metagenomic revolution and help of high-throughput sequencing technologies. Different ecological hypotheses explaining their high prevalence in soil and questioning their transfer rate to pathogens, in respect to these recent experimental results, will be discussed in the present review.

Environmental bacteria and especially soil bacteria are active producers of antibiotic molecules and most drugs used nowadays are isolated from saprophytic soil bacteria and these microorganisms have also evolved numerous resistance pathways leading to an arsenal of Antibiotic Resistance Genes Determinants (ARGD) known as the environmental resistome. A survey of ARGD prevalence is required in order to characterize this natural phenomenon with critical implications in our current infectious diseases management. In order to perform such analysis we compiled a set of 71 metagenomic datasets from various environmental origins: soils, oceans, lakes, human feces, indoor air, etc., and compared their sequences with a database of known antibiotic resistance gene determinants (ARGD). ARGD-annotated reads are found in every environment analyzed confirming their ubiquity. Soil is found to be the richest and shares a large part of ARGD with the human gut microbiome, indicating ARGD transfers between these environments. Experiments using qPCR and metagenomic DNA sequencing on soil samples from two sites with known and distinct antibiotic pollution history were conducted to understand how ARGD abundance and diversity in soil are affected when impacted by antibiotic molecules. The first site is a reference soil from a long-term experiment without history of antibiotic pollution (Rothamsted Park Grass, UK). Soil microcosms are setup with addition of either antibiotic-containg animal manure or pure molecules and incubated for 6 months to monitor changes in ARGD concentration following these perturbations. Our second study-site is a very remote settlement in French Guiana where antibiotics are available since recently and may have impacted the local soil microbial community. Soil samples are taken following a line-transect going from the village (antibiotic source) to 3km deep in the forest in a gradient of human-impact. Our results all confirm prevalence of ARGD in soil at significant abundance but also that ARGD distribution is more correlated to environmental factors such as soil type, microbial taxonomy composition or microcosms incubation conditions than antibiotic molecules exposure in both sites. Pathogens ARGD diversity is far lower than ARGD diversity found in the environment and not all the soil resistome is readily accessible for transfer. In order to characterize the soil mobile gene pool, a strategy is proposed to isolate specifically mobile DNA directly from the environment for sequencing purposes. Better knowledge on the microbial ecology factors limiting ARGD transfers to pathogens may greatly help us reduce the current threat on our limited medical antibiotic molecules resource., Les bactéries de l'environnement et du sol en particulier sont des producteurs actifs de molécules antibiotiques et les composés antibiotiques utilisés en médecine ont pour la plupart été isolés de bactéries saprophytes du sol qui ont elle mêmes développé une variété de mécanismes pour contrer les effets des antibiotiques conduisant à un arsenal de gènes de résistance à des antibiotiques dans l'environnement (ARGD). Une évaluation de l'abondance et de la diversité en terme de gènes de résistance à des antibiotiques a donc été conduite. Pour cette analyse, nous avons compilé 71 jeux de données de séquences d'ADN métagénomique environnementale variées: océans, et identifié des gènes de résistance pour chacun d'eux. Le sol est confirmé par cette étude comme un environnement extrêmement divers en terme de résistance à des antibiotiques. Cet étude in silico a été complété d'abord par une approche en microcosmes visant à étudier les effets soit de pollution soit par des molécules antibiotiques pures, soit par des effluents de ferme utilisés pour la fertilisation des sols. Les microcosmes de sols ont été incubés pendant 6 mois au laboratoire en conditions contrôlées. L'abondance de gènes de résistance à des antibiotiques a été évaluée au cours du temps par PCR quantitative. Une seconde étude visant à évaluer l'impact de la consommation de molécules antibiotiques par une population humaine sur son environnement immédiat, dont le sol, a été entreprise. Le village de Trois-Sauts est situé sur les berges du haut-Oyapock en Guyane Française. Les prescriptions antibiotiques sont très récentes dans cette région et les molécules distribuées ont été précisément répertoriées. Un transect de sol de 3km a été échantillonné chaque 600m afin de vérifier l'existence d'un gradient d'anthropisation entre le village (0m) et les échantillons de forêt les plus distants (3000m). Tous nos résultats confirment la présence à une forte abondance de gènes de résistance à des antibiotiques dans l'environnement, et en particulier dans le sol. Les facteurs à l'origine de la sélection et de la dissémination des gènes de résistance restent cependant difficiles à appréhender dans des environnements aussi complexes. C'est cependant avec une meilleure compréhension des phénomènes conduisant à l'émergence et à la dissémination des gènes de résistance à des antibiotiques au sein des flores pathogènes, depuis leur réservoir environnemental, que nous pourrons agir en vue de préserver les antibiotiques encore actifs aujourd'hui et ceux encore à développer.

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