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Mzee T, Kumburu H, Kazimoto T, Leekitcharoenphon P, van Zwetselaar M, Masalu R, Mlaganile T, Sonda T, Wadugu B, Mushi I, Aarestrup FM, Matee M. Molecular Characterization of Staphylococcus aureus Isolated from Raw Milk and Humans in Eastern Tanzania: Genetic Diversity and Inter-Host Transmission. Microorganisms 2023; 11:1505. [PMID: 37375007 DOI: 10.3390/microorganisms11061505] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/22/2023] [Accepted: 04/23/2023] [Indexed: 06/29/2023] Open
Abstract
Staphylococcus aureus is a common cause of infection in humans and animals, including bovine mastitis, globally. The objective of this study was to genetically characterize a collection of S. aureus isolates recovered from milk and nasal swabs from humans with and without animal contact (bovine = 43, human = 12). Using whole genome sequencing (NextSeq550), isolates were sequence typed, screened for antimicrobial resistance and virulence genes and examined for possible inter-species host transmission. Multi locus sequence typing (MLST) and single nucleotide polymorphism (SNP)-based phylogeny revealed 14 different sequence types, including the following six novel sequence types: ST7840, 7841, 7845, 7846, 7847, and 7848. The SNP tree confirmed that MLST clustering occurred most commonly within CC97, CC5477, and CC152. ResFinder analysis revealed five common antibiotic resistance genes, namely tet(K), blaZ, dfrG, erm©, and str, encoding for different antibiotics. mecA was discovered in one human isolate only. Multidrug resistance was observed in 25% of the isolates, predominantly in CC152 (7/8) and CC121 (3/4). Known bovine S. aureus (CC97) were collected in humans and known human S. aureus lineages (CC152) were collected in cattle; additionally, when these were compared to bovine-isolated CC97 and human-isolated CC152, respectively, no genetic distinction could be observed. This is suggestive of inter-host transmission and supports the need for surveillance of the human-animal interface.
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Izquierdo-Lara RW, Heijnen L, Oude Munnink BB, Schapendonk CME, Elsinga G, Langeveld J, Post J, Prasad DK, Carrizosa C, Been F, van Beek J, Schilperoort R, Vriend R, Fanoy E, de Schepper EIT, Sikkema RS, Molenkamp R, Aarestrup FM, Medema G, Koopmans MPG, de Graaf M. Rise and fall of SARS-CoV-2 variants in Rotterdam: Comparison of wastewater and clinical surveillance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 873:162209. [PMID: 36796689 PMCID: PMC9927792 DOI: 10.1016/j.scitotenv.2023.162209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 06/04/2023]
Abstract
Monitoring of SARS-CoV-2 in wastewater (WW) is a promising tool for epidemiological surveillance, correlating not only viral RNA levels with the infection dynamics within the population, but also to viral diversity. However, the complex mixture of viral lineages in WW samples makes tracking of specific variants or lineages circulating in the population a challenging task. We sequenced sewage samples of 9 WW-catchment areas within the city of Rotterdam, used specific signature mutations from individual SARS-CoV-2 lineages to estimate their relative abundances in WW and compared them against those observed in clinical genomic surveillance of infected individuals between September 2020 and December 2021. We showed that especially for dominant lineages, the median of the frequencies of signature mutations coincides with the occurrence of those lineages in Rotterdam's clinical genomic surveillance. This, along with digital droplet RT-PCR targeting signature mutations of specific variants of concern (VOCs), showed that several VOCs emerged, became dominant and were replaced by the next VOC in Rotterdam at different time points during the study. In addition, single nucleotide variant (SNV) analysis provided evidence that spatio-temporal clusters can also be discerned from WW samples. We were able to detect specific SNVs in sewage, including one resulting in the Q183H amino acid change in the Spike gene, that was not captured by clinical genomic surveillance. Our results highlight the potential use of WW samples for genomic surveillance, increasing the set of epidemiological tools to monitor SARS-CoV-2 diversity.
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Duarte ASR, Marques AR, Andersen VD, Korsgaard HB, Mordhorst H, Møller FD, Petersen TN, Vigre H, Hald T, Aarestrup FM. Antimicrobial resistance monitoring in the Danish swine production by phenotypic methods and metagenomics from 1999 to 2018. Euro Surveill 2023; 28:2200678. [PMID: 37199989 PMCID: PMC10197494 DOI: 10.2807/1560-7917.es.2023.28.20.2200678] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 03/14/2023] [Indexed: 05/19/2023] Open
Abstract
BackgroundIn Denmark, antimicrobial resistance (AMR) in pigs has been monitored since 1995 by phenotypic approaches using the same indicator bacteria. Emerging methodologies, such as metagenomics, may allow novel surveillance ways.AimThis study aimed to assess the relevance of indicator bacteria (Escherichia coli and Enterococcus faecalis) for AMR surveillance in pigs, and the utility of metagenomics.MethodsWe collated existing data on AMR and antimicrobial use (AMU) from the Danish surveillance programme and performed metagenomics sequencing on caecal samples that had been collected/stored through the programme during 1999-2004 and 2015-2018. We compared phenotypic and metagenomics results regarding AMR, and the correlation of both with AMU.ResultsVia the relative abundance of AMR genes, metagenomics allowed to rank these genes as well as the AMRs they contributed to, by their level of occurrence. Across the two study periods, resistance to aminoglycosides, macrolides, tetracycline, and beta-lactams appeared prominent, while resistance to fosfomycin and quinolones appeared low. In 2015-2018 sulfonamide resistance shifted from a low occurrence category to an intermediate one. Resistance to glycopeptides consistently decreased during the entire study period. Outcomes of both phenotypic and metagenomics approaches appeared to positively correlate with AMU. Metagenomics further allowed to identify multiple time-lagged correlations between AMU and AMR, the most evident being that increased macrolide use in sow/piglets or fatteners led to increased macrolide resistance with a lag of 3-6 months.ConclusionWe validated the long-term usefulness of indicator bacteria and showed that metagenomics is a promising approach for AMR surveillance.
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Bogri A, Otani S, Aarestrup FM, Brinch C. Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.981377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
Abstract
The steep rise of infections caused by bacteria that are resistant to antimicrobial agents threatens global health. However, the association between antimicrobial use and the prevalence of resistance is not straightforward. Therefore, it is necessary to quantify the importance of additional factors that affect this relationship. We theoretically explore how the prevalence of resistance is affected by the combination of three factors: antimicrobial use, bacterial transmission, and fitness cost of resistance. We present a model that combines within-host, between-hosts and between-populations dynamics, built upon the competitive Lotka-Volterra equations. We developed the model in a manner that allows future experimental validation of the findings with single isolates in the laboratory. Each host may carry two strains (susceptible and resistant) that represent the host’s commensal microbiome and are not the target of the antimicrobial treatment. The model simulates a population of hosts who are treated periodically with antibiotics and transmit bacteria to each other. We show that bacterial transmission results in strain co-existence. Transmission disseminates resistant bacteria in the population, increasing the levels of resistance. Counterintuitively, when the cost of resistance is low, high transmission frequencies reduce resistance prevalence. Transmission between host populations leads to more similar resistance levels, increasing the susceptibility of the population with higher antimicrobial use. Overall, our results indicate that the interplay between bacterial transmission and strain fitness affects the prevalence of resistance in a non-linear way. We then place our results within the context of ecological theory, particularly on temporal niche partitioning and metapopulation rescue, and we formulate testable experimental predictions for future research.
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Andersen VD, Møller FD, Jensen MS, Aarestrup FM, Vigre H. The quantitative effect of antimicrobial usage in Danish pig farms on the abundance of antimicrobial resistance genes in slaughter pigs. Prev Vet Med 2023; 214:105899. [PMID: 36940534 DOI: 10.1016/j.prevetmed.2023.105899] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 02/27/2023] [Accepted: 03/07/2023] [Indexed: 03/21/2023]
Abstract
Research has long established the connection between antimicrobial use (AMU) and antimicrobial resistance (AMR) in production animals, and shown that the ceasing of AMU reduces AMR. Our previous study of Danish slaughter-pig production found a quantitative relationship between lifetime AMU and abundance of antimicrobial resistance genes (ARGs). This study aimed to generate further quantitative knowledge on how changes in AMU in farms influence the abundance of ARGs both with immediate effect and over time. The study included 83 farms that were visited from 1 to 5 times. From each visit, a pooled faecal sample was produced. The abundance of ARGs was obtained by metagenomics. We used two-level linear mixed models for estimating the effect of AMU on the abundance of ARGs against six antimicrobial classes. The lifetime AMU of each batch was calculated from usage during their three rearing periods; as piglets, weaners and slaughter pigs (rearing pathway). AMU at farm level was estimated as the mean lifetime AMU of the sampled batches from each farm. At batch level, AMU was measured as the deviation between the batch-specific lifetime AMU and the general mean lifetime AMU at the farm. For peroral tetracycline and macrolide use there was a significant quantitative linear effect on the abundance of ARGs in batches within individual farms, indicating an immediate effect of changed AMU from batch to batch within farms. These estimated effects between batches within farms were approximately 1/2-1/3 of the effect estimated between farms. For all antimicrobial classes, the effect of the mean farm-level AMU and the abundance of ARGs present in the faeces of slaughter pigs was significant. This effect was identified only for peroral use, except for lincosamides, where the effect was for parenteral use. The results also indicated that the abundance of ARGs against a specific antimicrobial class also increased by the peroral usage of one or several other antimicrobial classes, except for ARGs against beta-lactams. These effects were generally lower than the AMU effect of the specific antimicrobial class. Overall, the farm peroral mean lifetime AMU affected the abundance of ARGs at antimicrobial class level and abundance of ARGs of other classes. However, the difference of AMU of the slaughter-pig batches affected only the abundance of ARGs at the same antimicrobial class level in the same antimicrobial class. The results do not exclude that parenteral usage of antimicrobials may have an effect on the abundance of ARGs.
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Munk P, Brinch C, Møller FD, Petersen TN, Hendriksen RS, Seyfarth AM, Kjeldgaard JS, Svendsen CA, van Bunnik B, Berglund F, Larsson DGJ, Koopmans M, Woolhouse M, Aarestrup FM, Gibb K, Coventry K, Collignon P, Cassar S, Allerberger F, Begum A, Hossain ZZ, Worrell C, Vandenberg O, Pieters I, Victorien DT, Gutierrez ADS, Soria F, Grujić VR, Mazalica N, Rahube TO, Tagliati CA, Rodrigues D, Oliveira G, de Souza LCR, Ivanov I, Juste BI, Oumar T, Sopheak T, Vuthy Y, Ngandjio A, Nzouankeu A, Olivier ZAAJ, Yost CK, Kumar P, Brar SK, Tabo DA, Adell AD, Paredes-Osses E, Martinez MC, Cuadros-Orellana S, Ke C, Zheng H, Baisheng L, Lau LT, Chung T, Jiao X, Yu Y, JiaYong Z, Morales JFB, Valencia MF, Donado-Godoy P, Coulibaly KJ, Hrenovic J, Jergović M, Karpíšková R, Deogratias ZN, Elsborg B, Hansen LT, Jensen PE, Abouelnaga M, Salem MF, Koolmeister M, Legesse M, Eguale T, Heikinheimo A, Le Guyader S, Schaeffer J, Villacis JE, Sanneh B, Malania L, Nitsche A, Brinkmann A, Schubert S, Hesse S, Berendonk TU, Saba CKS, Mohammed J, Feglo PK, Banu RA, Kotzamanidis C, Lytras E, Lickes SA, Kocsis B, Solymosi N, Thorsteinsdottir TR, Hatha AM, Ballal M, Bangera SR, Fani F, Alebouyeh M, Morris D, O’Connor L, Cormican M, Moran-Gilad J, Battisti A, Diaconu EL, Corno G, Di Cesare A, Alba P, Hisatsune J, Yu L, Kuroda M, Sugai M, Kayama S, Shakenova Z, Kiiyukia C, Ng’eno E, Raka L, Jamil K, Fakhraldeen SA, Alaati T, Bērziņš A, Avsejenko J, Kokina K, Streikisa M, Bartkevics V, Matar GM, Daoud Z, Pereckienė A, Butrimaite-Ambrozeviciene C, Penny C, Bastaraud A, Rasolofoarison T, Collard JM, Samison LH, Andrianarivelo MR, Banda DL, Amin A, Rajandas H, Parimannan S, Spiteri D, Haber MV, Santchurn SJ, Vujacic A, Djurovic D, Bouchrif B, Karraouan B, Vubil DC, Pal P, Schmitt H, van Passel M, Jeunen GJ, Gemmell N, Chambers ST, Mendoza FP, Huete-Pιrez J, Vilchez S, Ahmed AO, Adisa IR, Odetokun IA, Fashae K, Sørgaard AM, Wester AL, Ryrfors P, Holmstad R, Mohsin M, Hasan R, Shakoor S, Gustafson NW, Schill CH, Rojas MLZ, Velasquez JE, Magtibay BB, Catangcatang K, Sibulo R, Yauce FC, Wasyl D, Manaia C, Rocha J, Martins J, Álvaro P, Di Yoong Wen D, Shin H, Hur HG, Yoon S, Bosevska G, Kochubovski M, Cojocaru R, Burduniuc O, Hong PY, Perry MR, Gassama A, Radosavljevic V, Tay MYF, Zuniga-Montanez R, Wuertz S, Gavačová D, Pastuchová K, Truska P, Trkov M, Keddy K, Esterhuyse K, Song MJ, Quintela-Baluja M, Lopez MG, Cerdà-Cuéllar M, Perera RRDP, Bandara NKBKRGW, Premasiri HI, Pathirage S, Charlemagne K, Rutgersson C, Norrgren L, Örn S, Boss R, Van der Heijden T, Hong YP, Kumburu HH, Mdegela RH, Hounmanou YMG, Chonsin K, Suthienkul O, Thamlikitkul V, de Roda Husman AM, Bidjada B, Njanpop-Lafourcade BM, Nikiema-Pessinaba SC, Levent B, Kurekci C, Ejobi F, Kalule JB, Thomsen J, Obaidi O, Jassim LM, Moore A, Leonard A, Graham DW, Bunce JT, Zhang L, Gaze WH, Lefor B, Capone D, Sozzi E, Brown J, Meschke JS, Sobsey MD, Davis M, Beck NK, Sukapanpatharam P, Truong P, Lilienthal R, Kang S, Wittum TE, Rigamonti N, Baklayan P, Van CD, Tran DMN, Do Phuc N, Kwenda G, Larsson DGJ, Koopmans M, Woolhouse M, Aarestrup FM. Author Correction: Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance. Nat Commun 2023; 14:178. [PMID: 36635285 PMCID: PMC9837105 DOI: 10.1038/s41467-023-35890-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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Ostenfeld LJ, Munk P, Aarestrup FM, Otani S. Detection of specific uncultured bacteriophages by fluorescence in situ hybridisation in pig microbiome. PLoS One 2023; 18:e0283676. [PMID: 36996123 PMCID: PMC10062541 DOI: 10.1371/journal.pone.0283676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 03/14/2023] [Indexed: 03/31/2023] Open
Abstract
Microbial communities have huge impacts on their ecosystems and local environments spanning from marine and soil communities to the mammalian gut. Bacteriophages (phages) are important drivers of population control and diversity in the community, but our understanding of complex microbial communities is halted by biased detection techniques. Metagenomics have provided a method of novel phage discovery independent of in vitro culturing techniques and have revealed a large proportion of understudied phages. Here, five jumbophage genomes, that were previously assembled in silico from pig faecal metagenomes, are detected and observed directly in their natural environment using a modified phageFISH approach, and combined with methods to decrease bias against large-sized phages (e.g., jumbophages). These phages are uncultured with unknown hosts. The specific phages were detected by PCR and fluorescent in situ hybridisation in their original faecal samples as well as across other faecal samples. Co-localisation of bacterial signals and phage signals allowed detection of the different stages of phage life cycle. All phages displayed examples of early infection, advanced infection, burst, and free phages. To our knowledge, this is the first detection of jumbophages in faeces, which were investigated independently of culture, host identification, and size, and based solely on the genome sequence. This approach opens up opportunities for characterisation of novel in silico phages in vivo from a broad range of gut microbiomes.
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Aytan-Aktug D, Grigorjev V, Szarvas J, Clausen PTLC, Munk P, Nguyen M, Davis JJ, Aarestrup FM, Lund O. SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies. Microbiol Spectr 2022; 10:e0264122. [PMID: 36377945 PMCID: PMC9769690 DOI: 10.1128/spectrum.02641-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 11/01/2022] [Indexed: 11/16/2022] Open
Abstract
High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/services/SourceFinder/). IMPORTANCE Extra-chromosomal genes encoding antimicrobial resistance, metal resistance, and virulence provide selective advantages for bacterial survival under stress conditions and pose serious threats to human and animal health. These accessory genes can impact the composition of microbiomes by providing selective advantages to their hosts. Accurately identifying extra-chromosomal elements in genome sequence data are critical for understanding gene dissemination trajectories and taking preventative measures. Therefore, in this study, we developed a random forest classifier for identifying the source of bacterial chromosomal, plasmid, and bacteriophage sequences.
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Munk P, Brinch C, Møller FD, Petersen TN, Hendriksen RS, Seyfarth AM, Kjeldgaard JS, Svendsen CA, van Bunnik B, Berglund F, Larsson DGJ, Koopmans M, Woolhouse M, Aarestrup FM. Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance. Nat Commun 2022; 13:7251. [PMID: 36456547 PMCID: PMC9715550 DOI: 10.1038/s41467-022-34312-7] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/20/2022] [Indexed: 12/03/2022] Open
Abstract
Antimicrobial resistance (AMR) is a major threat to global health. Understanding the emergence, evolution, and transmission of individual antibiotic resistance genes (ARGs) is essential to develop sustainable strategies combatting this threat. Here, we use metagenomic sequencing to analyse ARGs in 757 sewage samples from 243 cities in 101 countries, collected from 2016 to 2019. We find regional patterns in resistomes, and these differ between subsets corresponding to drug classes and are partly driven by taxonomic variation. The genetic environments of 49 common ARGs are highly diverse, with most common ARGs carried by multiple distinct genomic contexts globally and sometimes on plasmids. Analysis of flanking sequence revealed ARG-specific patterns of dispersal limitation and global transmission. Our data furthermore suggest certain geographies are more prone to transmission events and should receive additional attention.
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Teseo S, Otani S, Brinch C, Leroy S, Ruiz P, Desvaux M, Forano E, Aarestrup FM, Sapountzis P. A global phylogenomic and metabolic reconstruction of the large intestine bacterial community of domesticated cattle. MICROBIOME 2022; 10:155. [PMID: 36155629 PMCID: PMC9511753 DOI: 10.1186/s40168-022-01357-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/24/2022] [Indexed: 05/30/2023]
Abstract
BACKGROUND The large intestine is a colonization site of beneficial microbes complementing the nutrition of cattle but also of zoonotic and animal pathogens. Here, we present the first global gene catalog of cattle fecal microbiomes, a proxy of the large intestine microbiomes, from 436 metagenomes from six countries. RESULTS Phylogenomics suggested that the reconstructed genomes and their close relatives form distinct branches and produced clustering patterns that were reminiscent of the metagenomics sample origin. Bacterial taxa had distinct metabolic profiles, and complete metabolic pathways were mainly linked to carbohydrates and amino acids metabolism. Dietary changes affected the community composition, diversity, and potential virulence. However, predicted enzymes, which were part of complete metabolic pathways, remained present, albeit encoded by different microbes. CONCLUSIONS Our findings provide a global insight into the phylogenetic relationships and the metabolic potential of a rich yet understudied bacterial community and suggest that it provides valuable services to the host. However, we tentatively infer that members of that community are not irreplaceable, because similar to previous findings, symbionts of complex bacterial communities of mammals are expendable if there are substitutes that can perform the same task. Video Abstract.
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Sengeruan LP, van Zwetselaar M, Kumburu H, Aarestrup FM, Kreppel K, Sauli E, Sonda T. Plasmid characterization in bacterial isolates of public health relevance in a tertiary healthcare facility in Kilimanjaro, Tanzania. J Glob Antimicrob Resist 2022; 30:384-389. [DOI: 10.1016/j.jgar.2022.06.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/15/2022] [Accepted: 06/27/2022] [Indexed: 10/17/2022] Open
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Reda RM, Maricchiolo G, Quero GM, Basili M, Aarestrup FM, Pansera L, Mirto S, Abd El-Fattah AH, Alagawany M, Abdel Rahman AN. Rice protein concentrate as a fish meal substitute in Oreochromis niloticus: Effects on immune response, intestinal cytokines, Aeromonas veronii resistance, and gut microbiota composition. FISH & SHELLFISH IMMUNOLOGY 2022; 126:237-250. [PMID: 35654384 DOI: 10.1016/j.fsi.2022.05.048] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 05/13/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
The potential of rice protein concentrate (RPC) to substitute fishmeal (FM) protein in the diet of Oreochromis niloticus was assessed in a five-month-long feeding trial. Fishmeal protein was replaced by RPC at rates of 0% (control), 25%, 50%, and 75% (RPC0, RPC25, RPC50, and RPC75, respectively). RPC25 had no significant effect on antioxidant capacity (total antioxidant capacity; superoxide dismutase, catalase, and glutathione peroxidase activities) and immune indices (lysozyme, nitric oxide, antiprotease, and bactericidal activities) after one, two, and five months of feeding, while the values for these parameters were significantly lower in the RPC75 group compared to those in the RPC0 group. The RPC25 group showed higher mRNA levels of the intestinal cytokines IL-1β, IL-10β, TGF-β, and TNF-α than the control group. In fish affected by Aeromonas veronii, the highest significant cumulative mortality was recorded in the RPC75 group, followed by the RPC50, RPC25, and control groups. Gut microbiome analyses showed a reduction in microbial diversity in response to the addition of RPC, regardless of the RPC content, and the composition of the community of the RPC samples differed from that of the control. RPC-enriched diets resulted in higher relative abundances of Bacteroidetes and Fusobacteria in the gut compared to that in the gut of the control fish. In summary, RPC can be used to replace up to 25% of the FM protein in the diet of O. niloticus, while improving the antioxidant capacity, immunocompetence, and disease resistance of the fish.
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Rebelo AR, Bortolaia V, Leekitcharoenphon P, Hansen DS, Nielsen HL, Ellermann-Eriksen S, Kemp M, Røder BL, Frimodt-Møller N, Søndergaard TS, Coia JE, Østergaard C, Westh H, Aarestrup FM. One Day in Denmark: Comparison of Phenotypic and Genotypic Antimicrobial Susceptibility Testing in Bacterial Isolates From Clinical Settings. Front Microbiol 2022; 13:804627. [PMID: 35756053 PMCID: PMC9226621 DOI: 10.3389/fmicb.2022.804627] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Antimicrobial susceptibility testing (AST) should be fast and accurate, leading to proper interventions and therapeutic success. Clinical microbiology laboratories rely on phenotypic methods, but the continuous improvement and decrease in the cost of whole-genome sequencing (WGS) technologies make them an attractive alternative. Studies evaluating the performance of WGS-based prediction of antimicrobial resistance (AMR) for selected bacterial species have shown promising results. There are, however, significant gaps in the literature evaluating the applicability of WGS as a diagnostics method in real-life clinical settings against the range of bacterial pathogens experienced there. Thus, we compared standard phenotypic AST results with WGS-based predictions of AMR profiles in bacterial isolates without preselection of defined species, to evaluate the applicability of WGS as a diagnostics method in clinical settings. We collected all bacterial isolates processed by all Danish Clinical Microbiology Laboratories in 1 day. We randomly selected 500 isolates without any preselection of species. We performed AST through standard broth microdilution (BMD) for 488 isolates (n = 6,487 phenotypic AST results) and compared results with in silico antibiograms obtained through WGS (Illumina NextSeq) followed by bioinformatics analyses using ResFinder 4.0 (n = 5,229 comparisons). A higher proportion of AMR was observed for Gram-negative bacteria (10.9%) than for Gram-positive bacteria (6.1%). Comparison of BMD with WGS data yielded a concordance of 91.7%, with discordant results mainly due to phenotypically susceptible isolates harboring genetic AMR determinants. These cases correspond to 6.2% of all isolate-antimicrobial combinations analyzed and to 6.8% of all phenotypically susceptible combinations. We detected fewer cases of phenotypically resistant isolates without any known genetic resistance mechanism, particularly 2.1% of all combinations analyzed, which corresponded to 26.4% of all detected phenotypic resistances. Most discordances were observed for specific combinations of species-antimicrobial: macrolides and tetracycline in streptococci, ciprofloxacin and β-lactams in combination with β-lactamase inhibitors in Enterobacterales, and most antimicrobials in Pseudomonas aeruginosa. WGS has the potential to be used for surveillance and routine clinical microbiology. However, in clinical microbiology settings and especially for certain species and antimicrobial agent combinations, further developments in AMR gene databases are needed to ensure higher concordance between in silico predictions and expected phenotypic AMR profiles.
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Janes VA, Matamoros S, Munk P, Clausen PTLC, Koekkoek SM, Koster LAM, Jakobs ME, de Wever B, Visser CE, Aarestrup FM, Lund O, de Jong MD, Bossuyt PMM, Mende DR, Schultsz C. Metagenomic DNA sequencing for semi-quantitative pathogen detection from urine: a prospective, laboratory-based, proof-of-concept study. THE LANCET MICROBE 2022; 3:e588-e597. [DOI: 10.1016/s2666-5247(22)00088-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 03/11/2022] [Accepted: 03/31/2022] [Indexed: 10/18/2022] Open
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Poulsen CS, Ekstrøm CT, Aarestrup FM, Pamp SJ. Library Preparation and Sequencing Platform Introduce Bias in Metagenomic-Based Characterizations of Microbiomes. Microbiol Spectr 2022; 10:e0009022. [PMID: 35289669 PMCID: PMC9045301 DOI: 10.1128/spectrum.00090-22] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 02/22/2022] [Indexed: 11/20/2022] Open
Abstract
Metagenomics is increasingly used to describe microbial communities in biological specimens. Ideally, the steps involved in the processing of the biological specimens should not change the microbiome composition in a way that it could lead to false interpretations of inferred microbial community composition. Common steps in sample preparation include sample collection, storage, DNA isolation, library preparation, and DNA sequencing. Here, we assess the effect of three library preparation kits and two DNA sequencing platforms. Of the library preparation kits, one involved a PCR step (Nextera), and two were PCR free (NEXTflex and KAPA). We sequenced the libraries on Illumina HiSeq and NextSeq platforms. As example microbiomes, two pig fecal samples and two sewage samples of which aliquots were stored at different storage conditions (immediate processing and storage at -80°C) were assessed. All DNA isolations were performed in duplicate, totaling 80 samples, excluding controls. We found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. The different sequencing platforms introduced more variation than library preparation and freezing the samples. The results highlight that all sample processing steps need to be considered when comparing studies. Standardization of sample processing is key to generating comparable data within a study, and comparisons of differently generated data, such as in a meta-analysis, should be performed cautiously. IMPORTANCE Previous research has reported effects of sample storage conditions and DNA isolation procedures on metagenomics-based microbiome composition; however, the effect of library preparation and DNA sequencing in metagenomics has not been thoroughly assessed. Here, we provide evidence that library preparation and sequencing platform introduce systematic biases in the metagenomic-based characterization of microbial communities. These findings suggest that library preparation and sequencing are important parameters to keep consistent when aiming to detect small changes in microbiome community structure. Overall, we recommend that all samples in a microbiome study are processed in the same way to limit unwanted variations that could lead to false conclusions. Furthermore, if we are to obtain a more holistic insight from microbiome data generated around the world, we will need to provide more detailed sample metadata, including information about the different sample processing procedures, together with the DNA sequencing data at the public repositories.
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Aytan-Aktug D, Clausen PTLC, Szarvas J, Munk P, Otani S, Nguyen M, Davis JJ, Lund O, Aarestrup FM. PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest. mSystems 2022; 7:e0118021. [PMID: 35382558 PMCID: PMC9040769 DOI: 10.1128/msystems.01180-21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 03/16/2022] [Indexed: 11/20/2022] Open
Abstract
Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning-specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). IMPORTANCE Antimicrobial resistance is a global health threat to humans and animals, causing high mortality and morbidity while effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes that can encode antimicrobial resistance and virulence. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detection of the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution as well as taking effective countermeasures against antimicrobial resistance.
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Larsen J, Raisen CL, Ba X, Sadgrove NJ, Padilla-González GF, Simmonds MSJ, Loncaric I, Kerschner H, Apfalter P, Hartl R, Deplano A, Vandendriessche S, Černá Bolfíková B, Hulva P, Arendrup MC, Hare RK, Barnadas C, Stegger M, Sieber RN, Skov RL, Petersen A, Angen Ø, Rasmussen SL, Espinosa-Gongora C, Aarestrup FM, Lindholm LJ, Nykäsenoja SM, Laurent F, Becker K, Walther B, Kehrenberg C, Cuny C, Layer F, Werner G, Witte W, Stamm I, Moroni P, Jørgensen HJ, de Lencastre H, Cercenado E, García-Garrote F, Börjesson S, Hæggman S, Perreten V, Teale CJ, Waller AS, Pichon B, Curran MD, Ellington MJ, Welch JJ, Peacock SJ, Seilly DJ, Morgan FJE, Parkhill J, Hadjirin NF, Lindsay JA, Holden MTG, Edwards GF, Foster G, Paterson GK, Didelot X, Holmes MA, Harrison EM, Larsen AR. Emergence of methicillin resistance predates the clinical use of antibiotics. Nature 2022; 602:135-141. [PMID: 34987223 PMCID: PMC8810379 DOI: 10.1038/s41586-021-04265-w] [Citation(s) in RCA: 110] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 11/18/2021] [Indexed: 12/26/2022]
Abstract
The discovery of antibiotics more than 80 years ago has led to considerable improvements in human and animal health. Although antibiotic resistance in environmental bacteria is ancient, resistance in human pathogens is thought to be a modern phenomenon that is driven by the clinical use of antibiotics1. Here we show that particular lineages of methicillin-resistant Staphylococcus aureus-a notorious human pathogen-appeared in European hedgehogs in the pre-antibiotic era. Subsequently, these lineages spread within the local hedgehog populations and between hedgehogs and secondary hosts, including livestock and humans. We also demonstrate that the hedgehog dermatophyte Trichophyton erinacei produces two β-lactam antibiotics that provide a natural selective environment in which methicillin-resistant S. aureus isolates have an advantage over susceptible isolates. Together, these results suggest that methicillin resistance emerged in the pre-antibiotic era as a co-evolutionary adaptation of S. aureus to the colonization of dermatophyte-infected hedgehogs. The evolution of clinically relevant antibiotic-resistance genes in wild animals and the connectivity of natural, agricultural and human ecosystems demonstrate that the use of a One Health approach is critical for our understanding and management of antibiotic resistance, which is one of the biggest threats to global health, food security and development.
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Florensa AF, Kaas RS, Clausen PTLC, Aytan-Aktug D, Aarestrup FM. ResFinder - an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes. Microb Genom 2022; 8. [PMID: 35072601 PMCID: PMC8914360 DOI: 10.1099/mgen.0.000748] [Citation(s) in RCA: 119] [Impact Index Per Article: 59.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Antimicrobial resistance (AMR) is one of the most important health threats globally. The ability to accurately identify resistant bacterial isolates and the individual antimicrobial resistance genes (ARGs) is essential for understanding the evolution and emergence of AMR and to provide appropriate treatment. The rapid developments in next-generation sequencing technologies have made this technology available to researchers and microbiologists at routine laboratories around the world. However, tools available for those with limited experience with bioinformatics are lacking, especially to enable researchers and microbiologists in low- and middle-income countries (LMICs) to perform their own studies. The CGE-tools (Center for Genomic Epidemiology) including ResFinder (https://cge.cbs.dtu.dk/services/ResFinder/) was developed to provide freely available easy to use online bioinformatic tools allowing inexperienced researchers and microbiologists to perform simple bioinformatic analyses. The main purpose was and is to provide these solutions for people involved in frontline diagnosis especially in LMICs. Since its original publication in 2012, ResFinder has undergone a number of improvements including improvement of the code and databases, inclusion of point mutations for selected bacterial species and predictions of phenotypes also for selected species. As of 28 September 2021, 820 803 analyses have been performed using ResFinder from 61 776 IP-addresses in 171 countries. ResFinder clearly fulfills a need for several people around the globe and we hope to be able to continue to provide this service free of charge in the future. We also hope and expect to provide further improvements including phenotypic predictions for additional bacterial species.
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Poulsen CS, Kaas RS, Aarestrup FM, Pamp SJ. Standard Sample Storage Conditions Have an Impact on Inferred Microbiome Composition and Antimicrobial Resistance Patterns. Microbiol Spectr 2021; 9:e0138721. [PMID: 34612701 DOI: 10.1101/2021.05.24.445395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2023] Open
Abstract
Storage of biological specimens is crucial in the life and medical sciences. Storage conditions for samples can be different for a number of reasons, and it is unclear what effect this can have on the inferred microbiome composition in metagenomics analyses. Here, we assess the effect of common storage temperatures (deep freezer, -80°C; freezer, -20°C; refrigerator, 5°C; room temperature, 22°C) and storage times (immediate sample processing, 0 h; next day, 16 h; over weekend, 64 h; longer term, 4, 8, and 12 months) as well as repeated sample freezing and thawing (2 to 4 freeze-thaw cycles). We examined two different pig feces and sewage samples, unspiked and spiked with a mock community, in triplicate, respectively, amounting to a total of 438 samples (777 Gbp; 5.1 billion reads). Storage conditions had a significant and systematic effect on the taxonomic and functional composition of microbiomes. Distinct microbial taxa and antimicrobial resistance classes were, in some situations, similarly affected across samples, while others were not, suggesting an impact of individual inherent sample characteristics. With an increasing number of freeze-thaw cycles, an increasing abundance of Firmicutes, Actinobacteria, and eukaryotic microorganisms was observed. We provide recommendations for sample storage and strongly suggest including more detailed information in the metadata together with the DNA sequencing data in public repositories to better facilitate meta-analyses and reproducibility of findings. IMPORTANCE Previous research has reported effects of DNA isolation, library preparation, and sequencing technology on metagenomics-based microbiome composition; however, the effect of biospecimen storage conditions has not been thoroughly assessed. We examined the effect of common sample storage conditions on metagenomics-based microbiome composition and found significant and, in part, systematic effects. Repeated freeze-thaw cycles could be used to improve the detection of microorganisms with more rigid cell walls, including parasites. We provide a data set that could also be used for benchmarking algorithms to identify and correct for unwanted batch effects. Overall, the findings suggest that all samples of a microbiome study should be stored in the same way. Furthermore, there is a need to mandate more detailed information about sample storage and processing be published together with DNA sequencing data at the International Nucleotide Sequence Database Collaboration (ENA/EBI, NCBI, DDBJ) or other repositories.
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Poulsen CS, Kaas RS, Aarestrup FM, Pamp SJ. Standard Sample Storage Conditions Have an Impact on Inferred Microbiome Composition and Antimicrobial Resistance Patterns. Microbiol Spectr 2021; 9:e0138721. [PMID: 34612701 PMCID: PMC8510183 DOI: 10.1128/spectrum.01387-21] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/02/2021] [Indexed: 12/11/2022] Open
Abstract
Storage of biological specimens is crucial in the life and medical sciences. Storage conditions for samples can be different for a number of reasons, and it is unclear what effect this can have on the inferred microbiome composition in metagenomics analyses. Here, we assess the effect of common storage temperatures (deep freezer, -80°C; freezer, -20°C; refrigerator, 5°C; room temperature, 22°C) and storage times (immediate sample processing, 0 h; next day, 16 h; over weekend, 64 h; longer term, 4, 8, and 12 months) as well as repeated sample freezing and thawing (2 to 4 freeze-thaw cycles). We examined two different pig feces and sewage samples, unspiked and spiked with a mock community, in triplicate, respectively, amounting to a total of 438 samples (777 Gbp; 5.1 billion reads). Storage conditions had a significant and systematic effect on the taxonomic and functional composition of microbiomes. Distinct microbial taxa and antimicrobial resistance classes were, in some situations, similarly affected across samples, while others were not, suggesting an impact of individual inherent sample characteristics. With an increasing number of freeze-thaw cycles, an increasing abundance of Firmicutes, Actinobacteria, and eukaryotic microorganisms was observed. We provide recommendations for sample storage and strongly suggest including more detailed information in the metadata together with the DNA sequencing data in public repositories to better facilitate meta-analyses and reproducibility of findings. IMPORTANCE Previous research has reported effects of DNA isolation, library preparation, and sequencing technology on metagenomics-based microbiome composition; however, the effect of biospecimen storage conditions has not been thoroughly assessed. We examined the effect of common sample storage conditions on metagenomics-based microbiome composition and found significant and, in part, systematic effects. Repeated freeze-thaw cycles could be used to improve the detection of microorganisms with more rigid cell walls, including parasites. We provide a data set that could also be used for benchmarking algorithms to identify and correct for unwanted batch effects. Overall, the findings suggest that all samples of a microbiome study should be stored in the same way. Furthermore, there is a need to mandate more detailed information about sample storage and processing be published together with DNA sequencing data at the International Nucleotide Sequence Database Collaboration (ENA/EBI, NCBI, DDBJ) or other repositories.
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Aarestrup FM, Bonten M, Koopmans M. Pandemics- One Health preparedness for the next. LANCET REGIONAL HEALTH-EUROPE 2021; 9:100210. [PMID: 34642673 PMCID: PMC8495373 DOI: 10.1016/j.lanepe.2021.100210] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The majority of emerging infectious diseases originate in animals. Current routine surveillance is focused on known diseases and clinical syndromes, but the increasing likelihood of emerging disease outbreaks shows the critical importance of early detection of unusual illness or circulation of pathogens - prior to human disease manifestation. In this Viewpoint, we focus on one key pillar of preparedness—the need for early warning surveillance at the human, animal, environmental interface. The COVID-19 pandemic has revolutionized the scale of sequencing of pathogen genomes, and the current investments in global genomic surveillance offer great potential for a novel, truly integrated Disease X (with epidemic or pandemic potential) surveillance arm provided we do not make the mistake of developing them solely for the case at hand. Generic tools include metagenomic sequencing as a catch-all technique, rather than detection and sequencing protocols focusing on what we know. Developing agnostic or more targeted metagenomic sequencing to assess unusual disease in humans and animals, combined with random sampling of environmental samples capturing pathogen circulation is technically challenging, but could provide a true early warning system. Rather than rebuilding and reinforcing the pre-existing silo's, a real step forward would be to take the lessons learned and bring in novel essential partnerships in a One Health approach to preparedness.
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Perry MR, Lepper HC, McNally L, Wee BA, Munk P, Warr A, Moore B, Kalima P, Philip C, de Roda Husman AM, Aarestrup FM, Woolhouse MEJ, van Bunnik BAD. Secrets of the Hospital Underbelly: Patterns of Abundance of Antimicrobial Resistance Genes in Hospital Wastewater Vary by Specific Antimicrobial and Bacterial Family. Front Microbiol 2021; 12:703560. [PMID: 34566912 PMCID: PMC8461093 DOI: 10.3389/fmicb.2021.703560] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/10/2021] [Indexed: 01/05/2023] Open
Abstract
Background: Hospital wastewater is a major source of antimicrobial resistance (AMR) outflow into the environment. This study uses metagenomics to study how hospital clinical activity impacts antimicrobial resistance genes (ARGs) abundances in hospital wastewater. Methods: Sewage was collected over a 24-h period from multiple wastewater collection points (CPs) representing different specialties within a tertiary hospital site and simultaneously from community sewage works. High throughput shotgun sequencing was performed using Illumina HiSeq4000. ARG abundances were correlated to hospital antimicrobial usage (AMU), data on clinical activity and resistance prevalence in clinical isolates. Results: Microbiota and ARG composition varied between CPs and overall ARG abundance was higher in hospital wastewater than in community influent. ARG and microbiota compositions were correlated (Procrustes analysis, p=0.014). Total antimicrobial usage was not associated with higher ARG abundance in wastewater. However, there was a small positive association between resistance genes and antimicrobial usage matched to ARG phenotype (IRR 1.11, CI 1.06-1.16, p<0.001). Furthermore, analyzing carbapenem and vancomycin resistance separately indicated that counts of ARGs to these antimicrobials were positively associated with their increased usage [carbapenem rate ratio (RR) 1.91, 95% CI 1.01-3.72, p=0.07, and vancomycin RR 10.25, CI 2.32-49.10, p<0.01]. Overall, ARG abundance within hospital wastewater did not reflect resistance patterns in clinical isolates from concurrent hospital inpatients. However, for clinical isolates of the family Enterococcaceae and Staphylococcaceae, there was a positive relationship with wastewater ARG abundance [odds ratio (OR) 1.62, CI 1.33-2.00, p<0.001, and OR 1.65, CI 1.21-2.30, p=0.006 respectively]. Conclusion: We found that the relationship between hospital wastewater ARGs and antimicrobial usage or clinical isolate resistance varies by specific antimicrobial and bacterial family studied. One explanation, we consider is that relationships observed from multiple departments within a single hospital site will be detectable only for ARGs against parenteral antimicrobials uniquely used in the hospital setting. Our work highlights that using metagenomics to identify the full range of ARGs in hospital wastewater is a useful surveillance tool to monitor hospital ARG carriage and outflow and guide environmental policy on AMR.
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Nieuwenhuijse DF, Oude Munnink BB, Phan MVT, Munk P, Venkatakrishnan S, Aarestrup FM, Cotten M, Koopmans MPG. Author Correction: Setting a baseline for global urban virome surveillance in sewage. Sci Rep 2021; 11:17446. [PMID: 34433858 PMCID: PMC8387382 DOI: 10.1038/s41598-021-95934-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Mashe T, Leekitcharoenphon P, Mtapuri-Zinyowera S, Kingsley RA, Robertson V, Tarupiwa A, Kock MM, Makombe EP, Chaibva BV, Manangazira P, Phiri I, Nyadundu S, Chigwena CT, Mufoya LP, Thilliez G, Midzi S, Mwamakamba LW, Hamblion EL, Matheu J, Jensen JD, Aarestrup FM, Hendriksen RS, Ehlers MM. Salmonella enterica serovar Typhi H58 clone has been endemic in Zimbabwe from 2012 to 2019. J Antimicrob Chemother 2021; 76:1160-1167. [PMID: 33347558 DOI: 10.1093/jac/dkaa519] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 11/11/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Typhoid fever, caused by S. enterica ser. Typhi, continues to be a substantial health burden in developing countries. Little is known of the genotypic diversity of S. enterica ser. Typhi in Zimbabwe, but this is key for understanding the emergence and spread of this pathogen and devising interventions for its control. OBJECTIVES To report the molecular epidemiology of S. enterica ser. Typhi outbreak strains circulating from 2012 to 2019 in Zimbabwe, using comparative genomics. METHODS A review of typhoid cases records from 2012 to 2019 in Zimbabwe was performed. The phylogenetic relationship of outbreak isolates from 2012 to 2019 and emergence of antibiotic resistance was investigated by whole-genome sequence analysis. RESULTS A total 22 479 suspected typhoid cases, 760 confirmed cases were reported from 2012 to 2019 and 29 isolates were sequenced. The majority of the sequenced isolates were predicted to confer resistance to aminoglycosides, β-lactams, phenicols, sulphonamides, tetracycline and fluoroquinolones (including qnrS detection). The qnrS1 gene was associated with an IncN (subtype PST3) plasmid in 79% of the isolates. Whole-genome SNP analysis, SNP-based haplotyping and resistance determinant analysis showed that 93% of the isolates belonged to a single clade represented by multidrug-resistant H58 lineage I (4.3.1.1), with a maximum pair-wise distance of 22 SNPs. CONCLUSIONS This study has provided detailed genotypic characterization of the outbreak strain, identified as S. Typhi 4.3.1.1 (H58). The strain has reduced susceptibility to ciprofloxacin due to qnrS carried by an IncN (subtype PST3) plasmid resulting from ongoing evolution to full resistance.
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Ingham AC, Kielsen K, Mordhorst H, Ifversen M, Aarestrup FM, Müller KG, Pamp SJ. Microbiota long-term dynamics and prediction of acute graft-versus-host disease in pediatric allogeneic stem cell transplantation. MICROBIOME 2021; 9:148. [PMID: 34183060 PMCID: PMC8240369 DOI: 10.1186/s40168-021-01100-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/20/2021] [Indexed: 05/11/2023]
Abstract
BACKGROUND Patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT) exhibit changes in their gut microbiota and are experiencing a range of complications, including acute graft-versus-host disease (aGvHD). It is unknown if, when, and under which conditions a re-establishment of microbial and immunological homeostasis occurs. It is also unclear whether microbiota long-term dynamics occur at other body sites than the gut such as the mouth or nose. Moreover, it is not known whether the patients' microbiota prior to HSCT holds clues to whether the patient would suffer from severe complications subsequent to HSCT. Here, we take a holobiont perspective and performed an integrated host-microbiota analysis of the gut, oral, and nasal microbiota in 29 children undergoing allo-HSCT. RESULTS The bacterial diversity decreased in the gut, nose, and mouth during the first month and reconstituted again 1-3 months after allo-HSCT. The microbial community composition traversed three phases over 1 year. Distinct taxa discriminated the microbiota temporally at all three body sides, including Enterococcus spp., Lactobacillus spp., and Blautia spp. in the gut. Of note, certain microbial taxa appeared already changed in the patients prior to allo-HSCT as compared with healthy children. Acute GvHD occurring after allo-HSCT could be predicted from the microbiota composition at all three body sites prior to HSCT. The reconstitution of CD4+ T cells, TH17, and B cells was associated with distinct taxa of the gut, oral, and nasal microbiota. CONCLUSIONS This study reveals for the first time bacteria in the mouth and nose that may predict aGvHD. Monitoring of the microbiota at different body sites in HSCT patients and particularly through involvement of samples prior to transplantation may be of prognostic value and could assist in guiding personalized treatment strategies. The identification of distinct bacteria that have a potential to predict post-transplant aGvHD might provide opportunities for an improved preventive clinical management, including a modulation of microbiomes. The host-microbiota associations shared between several body sites might also support an implementation of more feasible oral and nasal swab sampling-based analyses. Altogether, the findings suggest that the microbiota and host factors together could provide actionable information to guiding precision medicine. Video Abstract.
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