A Sustainable Future
To highlight the vital role microbiology plays in delivering on the UN Sustainable Development Goals (SDGs), we have created a collection of must-read research on three critical aspects of the SDGs: antimicrobial resistance, soil health, and the circular economy.
Collection Contents
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Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study
Ronan M. Doyle, Denise M. O'Sullivan, Sean D. Aller, Sebastian Bruchmann, Taane Clark, Andreu Coello Pelegrin, Martin Cormican, Ernest Diez Benavente, Matthew J. Ellington, Elaine McGrath, Yair Motro, Thi Phuong Thuy Nguyen, Jody Phelan, Liam P. Shaw, Richard A. Stabler, Alex van Belkum, Lucy van Dorp, Neil Woodford, Jacob Moran-Gilad, Jim F. Huggett and Kathryn A. HarrisAntimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a ‘one-stop’ test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams (‘participants’) were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories.
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The diversity of ice algal communities on the Greenland Ice Sheet as revealed by oligotyping
More LessThe Arctic is being disproportionally affected by climate change compared with other geographic locations, and is currently experiencing unprecedented melt rates. The Greenland Ice Sheet (GrIS) can be regarded as the largest supraglacial ecosystem on Earth, and ice algae are the dominant primary producers on bare ice surfaces throughout the course of a melt season. Ice-algal-derived pigments cause a darkening of the ice surface, which in turn decreases albedo and increases melt rates. The important role of ice algae in changing melt rates has only recently been recognized, and we currently know little about their community compositions and functions. Here, we present the first analysis of ice algal communities across a 100 km transect on the GrIS by high-throughput sequencing and subsequent oligotyping of the most abundant taxa. Our data reveal an extremely low algal diversity with Ancylonema nordenskiöldii and a Mesotaenium species being by far the dominant taxa at all sites. We employed an oligotyping approach and revealed a hidden diversity not detectable by conventional clustering of operational taxonomic units and taxonomic classification. Oligotypes of the dominant taxa exhibit a site-specific distribution, which may be linked to differences in temperatures and subsequently the extent of the melting. Our results help to better understand the distribution patterns of ice algal communities that play a crucial role in the GrIS ecosystem.
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Deciphering the unexplored Leptospira diversity from soils uncovers genomic evolution to virulence
Despite recent advances in our understanding of the genomics of members of the genus Leptospira, little is known on how virulence has emerged in this heterogeneous bacterial genus as well as on the lifestyle of pathogenic members of the genus Leptospira outside animal hosts. Here, we isolated 12 novel species of the genus Leptospira from tropical soils, significantly increasing the number of known species to 35 and finding evidence of highly unexplored biodiversity in the genus. Extended comparative phylogenomics and pan-genome analyses at the genus level by incorporating 26 novel genomes, revealed that, the traditional leptospiral ‘pathogens’ cluster, as defined by their phylogenetic position, can be split in two groups with distinct virulence potential and accessory gene patterns. These genomic distinctions are strongly linked to the ability to cause or not severe infections in animal models and humans. Our results not only provide new insights into virulence evolution in the members of the genus Leptospira, but also lay the foundations for refining the classification of the pathogenic species.
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Determination of antimicrobial resistance of Enterococcus strains isolated from pigs and their genotypic characterization by method of amplification of DNA fragments surrounding rare restriction sites (ADSRRS fingerprinting)
More LessPurpose. In this study, we analysed phenotypic resistance profiles and their reflection in the genomic profiles of Enterococcus spp. strains isolated from pigs raised on different farms.
Methodology. Samples were collected from five pig farms (n=90 animals) and tested for Enterococcus. MICs of 12 antimicrobials were determined using the broth microdilution method, and epidemiological molecular analysis of strains belonging to selected species (faecalis, faecium and hirae) was performed using the ADSRRS-fingerprinting (amplification of DNA fragments surrounding rare restriction sites) method with a few modifications.
Results. The highest percentage of strains was resistant to tetracycline (73.4 %), erythromycin and tylosin (42.5 %) and rifampin (25.2 %), and a large number of strains exhibited high-level resistance to both kanamycin (25.2 %) and streptomycin (27.6 %). The strains of E. faecalis, E. faecium and E. hirae (n=184) revealed varied phenotypic resistance profiles, among which as many as seven met the criteria for multidrug resistance (30.4 % of strains tested). ADSRRS-fingerprinting analysis produced 17 genotypic profiles of individual strains which were correlated with their phenotypic resistance profiles. Only E. hirae strains susceptible to all of the chemotherapeutics tested had two different ADSRRS profiles. Moreover, eight animals were carriers of more than one genotype belonging to the same Enterococcus spp., mainly E. faecalis.
Conclusion. Given the possibility of transmission to humans of the high-resistance/multidrug resistance enterococci and the significant role of pigs as food animals in this process, it is necessary to introduce a multilevel control strategy by carrying out research on the resistance and molecular characteristics of indicator bacterial strains isolated from animals on individual farms.
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diskImageR: quantification of resistance and tolerance to antimicrobial drugs using disk diffusion assays
More LessMicrobial pathogens represent an increasing threat to human health. Although many infections can be successfully treated and cleared, drug resistance is a widespread problem. The existence of subpopulations of ‘tolerant’ cells (where a fraction of the population is able to grow above the population resistance level) may increase the rate of treatment failure; yet, existing methods to measure subpopulation effects are cumbersome. Here we describe diskImageR, a computational pipeline that analyses photographs of disk diffusion assays to determine the degree of drug susceptibility [the radius of inhibition, (RAD)], and two aspects of subpopulation growth [the fraction of growth (FoG) within the zone of inhibition, (ZOI), and the rate of change in growth from no drug to inhibitory drug concentrations, (SLOPE)]. diskImageR was used to examine the response of the human fungal pathogen Candida albicans to the antifungal drug fluconazole across different strain backgrounds and growth conditions. Disk diffusion assays performed under Clinical and Laboratory Standards Institute (CLSI) conditions led to more susceptibility and less tolerance than assays performed using rich medium conditions. We also used diskImageR to quantify the effects of three drugs in combination with fluconazole, finding that all three combinations affected tolerance, with the effect of one drug (doxycycline) being very strain dependent. The three drugs had different effects on susceptibility, with doxycycline generally having no effect, chloroquine generally increasing susceptibility and pyrvinium pamoate generally reducing susceptibility. The ability to simultaneously quantitate different aspects of microbial drug responses will facilitate the study of mechanisms of subpopulation responses in the presence of antimicrobial drugs.
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