Identification of 76 novel B1 metallo-β-lactamases through large-scale screening of genomic and metagenomic data

BACKGROUND

Metallo-β-lactamases are bacterial enzymes that provide resistance to carbapenems, the most potent class of antibiotics. These enzymes are commonly encoded on mobile genetic elements, which, together with their broad substrate spectrum and lack of clinically useful inhibitors, make them a particularly problematic class of antibiotic resistance determinants. We hypothesized that there is a large and unexplored reservoir of unknown metallo-β-lactamases, some of which may spread to pathogens, thereby threatening public health. The aim of this study was to identify novel metallo-β-lactamases of class B1, the most clinically important subclass of these enzymes.

RESULTS

Based on a new computational method using an optimized hidden Markov model, we analyzed over 10,000 bacterial genomes and plasmids together with more than 5 terabases of metagenomic data to identify novel metallo-β-lactamase genes. In total, 76 novel genes were predicted, forming 59 previously undescribed metallo-β-lactamase gene families. The ability to hydrolyze imipenem in an Escherichia coli host was experimentally confirmed for 18 of the 21 tested genes. Two of the novel B1 metallo-β-lactamase genes contained atypical zinc-binding motifs in their active sites, which were previously undescribed for metallo-β-lactamases. Phylogenetic analysis showed that B1 metallo-β-lactamases could be divided into five major groups based on their evolutionary origin. Our results also show that, except for one, all of the previously characterized mobile B1 β-lactamases are likely to have originated from chromosomal genes present in Shewanella spp. and other Proteobacterial species.

CONCLUSIONS

This study more than doubles the number of known B1 metallo-β-lactamases. The findings have further elucidated the diversity and evolutionary history of this important class of antibiotic resistance genes and prepare us for some of the challenges that may be faced in clinics in the future.

Using metagenomics to investigate human and environmental resistomes

Antibiotic resistance is a global health concern declared by the WHO as one of the largest threats to modern healthcare. In recent years, metagenomic DNA sequencing has started to be applied as a tool to study antibiotic resistance in different environments, including the human microbiota. However, a multitude of methods exist for metagenomic data analysis, and not all methods are suitable for the investigation of resistance genes, particularly if the desired outcome is an assessment of risks to human health. In this review, we outline the current state of methods for sequence handling, mapping to databases of resistance genes, statistical analysis and metagenomic assembly. In addition, we provide an overview of important considerations related to the analysis of resistance genes, and recommend some of the currently used tools and methods that are best equipped to inform research and clinical practice related to antibiotic resistance.

Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets

BACKGROUND

Fluoroquinolones are broad-spectrum antibiotics used to prevent and treat a wide range of bacterial infections. Plasmid-mediated qnr genes provide resistance to fluoroquinolones in many bacterial species and are increasingly encountered in clinical settings. Over the last decade, several families of qnr genes have been discovered and characterized, but their true prevalence and diversity still remain unclear. In particular, environmental and host-associated bacterial communities have been hypothesized to maintain a large and unknown collection of qnr genes that could be mobilized into pathogens.

RESULTS

In this study we used computational methods to screen genomes and metagenomes for novel qnr genes. In contrast to previous studies, we analyzed an almost 20-fold larger dataset comprising almost 13 terabases of sequence data. In total, 362,843 potential qnr gene fragments were identified, from which 611 putative qnr genes were reconstructed. These gene sequences included all previously described plasmid-mediated qnr gene families. Fifty-two of the 611 identified qnr genes were reconstructed from metagenomes, and 20 of these were previously undescribed. All of the novel qnr genes were assembled from metagenomes associated with aquatic environments. Nine of the novel genes were selected for validation, and six of the tested genes conferred consistently decreased susceptibility to ciprofloxacin when expressed in Escherichia coli.

CONCLUSIONS

The results presented in this study provide additional evidence for the ubiquitous presence of qnr genes in environmental microbial communities, expand the number of known qnr gene variants and further elucidate the diversity of this class of resistance genes. This study also strengthens the hypothesis that environmental bacterial communities act as sources of previously uncharacterized qnr genes.

Does antifouling paint select for antibiotic resistance?

There is concern that heavy metals and biocides contribute to the development of antibiotic resistance via co-selection. Most antifouling paints contain high amounts of such substances, which risks turning painted ship hulls into highly mobile refuges and breeding grounds for antibiotic-resistant bacteria. The objectives of this study were to start investigate if heavy-metal based antifouling paints can pose a risk for co-selection of antibiotic-resistant bacteria and, if so, identify the underlying genetic basis. Plastic panels with one side painted with copper and zinc-containing antifouling paint were submerged in a Swedish marina and biofilms from both sides of the panels were harvested after 2.5-4weeks. DNA was isolated from the biofilms and subjected to metagenomic sequencing. Biofilm bacteria were cultured on marine agar supplemented with tetracycline, gentamicin, copper sulfate or zinc sulfate. Biofilm communities from painted surfaces displayed lower taxonomic diversity and enrichment of Gammaproteobacteria. Bacteria from these communities showed increased resistance to both heavy metals and tetracycline but not to gentamicin. Significantly higher abundance of metal and biocide resistance genes was observed, whereas mobile antibiotic resistance genes were not enriched in these communities. In contrast, we found an enrichment of chromosomal RND efflux system genes, including such with documented ability to confer decreased susceptibility to both antibiotics and biocides/heavy metals. This was paralleled by increased abundances of integron-associated integrase and ISCR transposase genes. The results show that the heavy metal-based antifouling paint exerts a strong selection pressure on marine bacterial communities and can co-select for certain antibiotic-resistant bacteria, likely by favoring species and strains carrying genes that provide cross-resistance. Although this does not indicate an immediate risk for promotion of mobile antibiotic resistance, the clear increase of genes involved in mobilizing DNA provides a foundation for increased opportunities for gene transfer in such communities, which might also involve yet unknown resistance mechanisms.

HirBin: high-resolution identification of differentially abundant functions in metagenomes

Background

Gene-centric analysis of metagenomics data provides information about the biochemical functions present in a microbiome under a certain condition. The ability to identify significant differences in functions between metagenomes is dependent on accurate classification and quantification of the sequence reads (binning). However, biological effects acting on specific functions may be overlooked if the classes are too general.

Methods

Here we introduce High-Resolution Binning (HirBin), a new method for gene-centric analysis of metagenomes. HirBin combines supervised annotation with unsupervised clustering to bin sequence reads at a higher resolution. The supervised annotation is performed by matching sequence fragments to genes using well-established protein domains, such as TIGRFAM, PFAM or COGs, followed by unsupervised clustering where each functional domain is further divided into sub-bins based on sequence similarity. Finally, differential abundance of the sub-bins is statistically assessed.

Results

We show that HirBin is able to identify biological effects that are only present at more specific functional levels. Furthermore we show that changes affecting more specific functional levels are often diluted at the more general level and therefore overlooked when analyzed using standard binning approaches.

Conclusions

HirBin improves the resolution of the gene-centric analysis of metagenomes and facilitates the biological interpretation of the results. HirBin is implemented as a Python package and is freely available for download at http://bioinformatics.math.chalmers.se/hirbin.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3686-6) contains supplementary material, which is available to authorized users.

Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics

Methods for rapid and reliable microbial identification are essential in modern healthcare. The ability to detect and correctly identify pathogenic species and their resistance phenotype is necessary for accurate diagnosis and efficient treatment of infectious diseases. Bottom-up tandem mass spectrometry (MS) proteomics enables rapid characterization of large parts of the expressed genes of microorganisms. However, the generated data are highly fragmented, making downstream analyses complex. Here we present TCUP, a new computational method for typing and characterizing bacteria using proteomics data from bottom-up tandem MS. TCUP compares the generated protein sequence data to reference databases and automatically finds peptides suitable for characterization of taxonomic composition and identification of expressed antimicrobial resistance genes. TCUP was evaluated using several clinically relevant bacterial species (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae), using both simulated data generated by in silico peptide digestion and experimental proteomics data generated by liquid chromatography-tandem mass spectrometry (MS/MS). The results showed that TCUP performs correct peptide classifications at rates between 90.3 and 98.5% at the species level. The method was also able to estimate the relative abundances of individual species in mixed cultures. Furthermore, TCUP could identify expressed β-lactamases in an extended spectrum β-lactamase-producing (ESBL) E. coli strain, even when the strain was cultivated in the absence of antibiotics. Finally, TCUP is computationally efficient, easy to integrate in existing bioinformatics workflows, and freely available under an open source license for both Windows and Linux environments.

Elucidating selection processes for antibiotic resistance in sewage treatment plants using metagenomics

Sewage treatment plants (STPs) have repeatedly been suggested as "hotspots" for the emergence and dissemination of antibiotic-resistant bacteria. A critical question still unanswered is if selection pressures within STPs, caused by residual antibiotics or other co-selective agents, are sufficient to specifically promote resistance. To address this, we employed shotgun metagenomic sequencing of samples from different steps of the treatment process in three Swedish STPs. In parallel, concentrations of selected antibiotics, biocides and metals were analyzed. We found that concentrations of tetracycline and ciprofloxacin in the influent were above predicted concentrations for resistance selection, however, there was no consistent enrichment of resistance genes to any particular class of antibiotics in the STPs, neither for biocide and metal resistance genes. The most substantial change of the bacterial communities compared to human feces occurred already in the sewage pipes, manifested by a strong shift from obligate to facultative anaerobes. Through the treatment process, resistance genes against antibiotics, biocides and metals were not reduced to the same extent as fecal bacteria. The OXA-48 gene was consistently enriched in surplus and digested sludge. We find this worrying as OXA-48, still rare in Swedish clinical isolates, provides resistance to carbapenems, one of our most critically important classes of antibiotics. Taken together, metagenomics analyses did not provide clear support for specific antibiotic resistance selection. However, stronger selective forces affecting gross taxonomic composition, and with that resistance gene abundances, limit interpretability. Comprehensive analyses of resistant/non-resistant strains within relevant species are therefore warranted.

Direct identification of antibiotic resistance genes on single plasmid molecules using CRISPR/Cas9 in combination with optical DNA mapping

Bacterial plasmids are extensively involved in the rapid global spread of antibiotic resistance. We here present an assay, based on optical DNA mapping of single plasmids in nanofluidic channels, which provides detailed information about the plasmids present in a bacterial isolate. In a single experiment, we obtain the number of different plasmids in the sample, the size of each plasmid, an optical barcode that can be used to identify and trace the plasmid of interest and information about which plasmid that carries a specific resistance gene. Gene identification is done using CRISPR/Cas9 loaded with a guide-RNA (gRNA) complementary to the gene of interest that linearizes the circular plasmids at a specific location that is identified using the optical DNA maps. We demonstrate the principle on clinically relevant extended spectrum beta-lactamase (ESBL) producing isolates. We discuss how the gRNA sequence can be varied to obtain the desired information. The gRNA can either be very specific to identify a homogeneous group of genes or general to detect several groups of genes at the same time. Finally, we demonstrate an example where we use a combination of two gRNA sequences to identify carbapenemase-encoding genes in two previously not characterized clinical bacterial samples.

Variability in Metagenomic Count Data and Its Influence on the Identification of Differentially Abundant Genes

Metagenomics is the study of microorganisms in environmental and clinical samples using high-throughput sequencing of random fragments of their DNA. Since metagenomics does not require any prior culturing of isolates, entire microbial communities can be studied directly in their natural state. In metagenomics, the abundance of genes is quantified by sorting and counting the DNA fragments. The resulting count data are high-dimensional and affected by high levels of technical and biological noise that make the statistical analysis challenging. In this article, we introduce an hierarchical overdispersed Poisson model to explore the variability in metagenomic data. By analyzing three comprehensive data sets, we show that the gene-specific variability varies substantially between genes and is dependent on biological function. We also assess the power of identifying differentially abundant genes and show that incorrect assumptions about the gene-specific variability can lead to unacceptable high rates of false positives. Finally, we evaluate shrinkage approaches to improve the variance estimation and show that the prior choice significantly affects the statistical power. The results presented in this study further elucidate the complex variance structure of metagenomic data and provide suggestions for accurate and reliable identification of differentially abundant genes.

The structure and diversity of human, animal and environmental resistomes

BACKGROUND

Antibiotic resistance genes (ARGs) are widespread but cause problems only when present in pathogens. Environments where selection and transmission of antibiotic resistance frequently take place are likely to be characterized by high abundance and diversity of horizontally transferable ARGs. Large-scale quantitative data on ARGs is, however, lacking for most types of environments, including humans and animals, as is data on resistance genes to potential co-selective agents, such as biocides and metals. This paucity prevents efficient identification of risk environments.

RESULTS

We provide a comprehensive characterization of resistance genes, mobile genetic elements (MGEs) and bacterial taxonomic compositions for 864 metagenomes from humans (n = 350), animals (n = 145) and external environments (n = 369), all deeply sequenced using Illumina technology. Environment types showed clear differences in both resistance profiles and bacterial community compositions. Human and animal microbial communities were characterized by limited taxonomic diversity and low abundance and diversity of biocide/metal resistance genes and MGEs but a relatively high abundance of ARGs. In contrast, external environments showed consistently high taxonomic diversity which in turn was linked to high diversity of both biocide/metal resistance genes and MGEs. Water, sediment and soil generally carried low relative abundance and few varieties of known ARGs, whereas wastewater/sludge was on par with the human gut. The environments with the largest relative abundance and/or diversity of ARGs, including genes encoding resistance to last resort antibiotics, were those subjected to industrial antibiotic pollution and a limited set of deeply sequenced air samples from a Beijing smog event.

CONCLUSIONS

Our study identifies air and antibiotic-polluted environments as under-investigated transmission routes and reservoirs for antibiotic resistance. The high taxonomic and genetic diversity of external environments supports the hypothesis that these also form vast sources of unknown resistance genes, with potential to be transferred to pathogens in the future.

Biomarker responses in eelpouts from four coastal areas in Sweden, Denmark and Germany

To increase our understanding of possible chemical impacts on coastal fish populations in the Baltic Sea, Kattegat and Skagerrak, the viviparous eelpout (Zoarces viviparus) was used as sentinel species in two major sampling campaigns (spring and autumn) in 16 different coastal sites. Condition factor (CF), liver somatic index (LSI), gonad somatic index (GSI) were measured and the activity of the hepatic enzymes ethoxyresorufin-O-deethylase (EROD), glutathione reductase GR), glutathione S-transferase (GST), catalase (CAT) and muscular activity of acetylcholinesterase (AChE) were assessed. PAH metabolites in bile were also analyzed. The most notable finding in the data set was the low EROD activity in eelpouts collected at the relatively polluted region in Germany compared to the other regions, which could be due to an inhibition of the CYP1A-system or to adaptation to chronic exposure of pollutants in this area. Additionally, low AChE activity was noted in the German region in the autumn campaign and low AChE activity detected in the Danish region in the spring campaign. These differences suggest possible season-specific differences in the use and release of AChE-inhibiting chemicals in the Danish and German regions. Clustering of biomarkers on site level indicated a relationship between CF and GSI and suggested that sites with a high CF contained eelpout that put a larger effort into their larvae development. Clustering of the oxidative stress markers GR, GST and CAT on the individual level reflected a possible coordinated regulation of these enzymes. Overall, the results support the importance of taking into account general regional differences and seasonal variation in biomarker activity when monitoring and assessing the effects of pollution. Despite the expected seasonal variation for most of the measured endpoint, several markers (GSI, EROD and CF) vary similarly between all selected sites in both spring and autumn. This suggests that the differences between sites for these endpoints are independent of season.

HattCI: Fast and Accurate attC site Identification Using Hidden Markov Models

Integrons are genetic elements that facilitate the horizontal gene transfer in bacteria and are known to harbor genes associated with antibiotic resistance. The gene mobility in the integrons is governed by the presence of attC sites, which are 55 to 141-nucleotide-long imperfect inverted repeats. Here we present HattCI, a new method for fast and accurate identification of attC sites in large DNA data sets. The method is based on a generalized hidden Markov model that describes each core component of an attC site individually. Using twofold cross-validation experiments on a manually curated reference data set of 231 attC sites from class 1 and 2 integrons, HattCI showed high sensitivities of up to 91.9% while maintaining satisfactory false-positive rates. When applied to a metagenomic data set of 35 microbial communities from different environments, HattCI found a substantially higher number of attC sites in the samples that are known to contain more horizontally transferred elements. HattCI will significantly increase the ability to identify attC sites and thus integron-mediated genes in genomic and metagenomic data. HattCI is implemented in C and is freely available at http://bioinformatics.math.chalmers.se/HattCI .

Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics

Background

Metagenomics is the study of microbial communities by sequencing of genetic material directly from environmental or clinical samples. The genes present in the metagenomes are quantified by annotating and counting the generated DNA fragments. Identification of differentially abundant genes between metagenomes can provide important information about differences in community structure, diversity and biological function. Metagenomic data is however high-dimensional, contain high levels of biological and technical noise and have typically few biological replicates. The statistical analysis is therefore challenging and many approaches have been suggested to date.

Results

In this article we perform a comprehensive evaluation of 14 methods for identification of differentially abundant genes between metagenomes. The methods are compared based on the power to detect differentially abundant genes and their ability to correctly estimate the type I error rate and the false discovery rate. We show that sample size, effect size, and gene abundance greatly affect the performance of all methods. Several of the methods also show non-optimal model assumptions and biased false discovery rate estimates, which can result in too large numbers of false positives. We also demonstrate that the performance of several of the methods differs substantially between metagenomic data sequenced by different technologies.

Conclusions

Two methods, primarily designed for the analysis of RNA sequencing data (edgeR and DESeq2) together with a generalized linear model based on an overdispersed Poisson distribution were found to have best overall performance. The results presented in this study may serve as a guide for selecting suitable statistical methods for identification of differentially abundant genes in metagenomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2386-y) contains supplementary material, which is available to authorized users.

Resistance Mutations in gyrA and parC are Common in Escherichia Communities of both Fluoroquinolone-Polluted and Uncontaminated Aquatic Environments

Alterations in the target proteins of fluoroquinolones, especially in GyrA and ParC, are known to cause resistance. Here, we investigated environmental Escherichia communities to explore the possible link between the abundance of mutations, and the exposure to fluoroquinolones. Sediment samples were collected from a relatively pristine lake, up and downstream from a sewage treatment plant, and from several industrially polluted sites. The quinolone resistance-determining regions of gyrA and parC were analyzed using amplicon sequencing of metagenomic DNA. Five non-synonymous substitutions were present in all samples, and all of these mutations have been previously linked to fluoroquinolone resistance in Escherichia coli. In GyrA, substitutions S83L and D87N were on average detected at frequencies of 86 and 32%, respectively, and 31% of all amplicons encoded both substitutions. In ParC, substitutions S80I, E84G, and E84V were detected in 42, 0.9, and 6.0% of the amplicons, respectively, and 6.5% encoded double substitutions. There was no significant correlation between the level of fluoroquinolone pollution and the relative abundance of resistance mutations, with the exception of the most polluted site, which showed the highest abundance of said substitutions in both genes. Our results demonstrate that resistance mutations can be common in environmental Escherichia, even in the absence of a fluoroquinolone selective pressure.

Co-occurrence of resistance genes to antibiotics, biocides and metals reveals novel insights into their co-selection potential

BACKGROUND

Antibacterial biocides and metals can co-select for antibiotic resistance when bacteria harbour resistance or tolerance genes towards both types of compounds. Despite numerous case studies, systematic and quantitative data on co-occurrence of such genes on plasmids and chromosomes is lacking, as is knowledge on environments and bacterial taxa that tend to carry resistance genes to such compounds. This effectively prevents identification of risk scenarios. Therefore, we aimed to identify general patterns for which biocide/metal resistance genes (BMRGs) and antibiotic resistance genes (ARGs) that tend to occur together. We also aimed to quantify co-occurrence of resistance genes in different environments and taxa, and investigate to what extent plasmids carrying both types of genes are conjugative and/or are carrying toxin-antitoxin systems.

RESULTS

Co-occurrence patterns of resistance genes were derived from publicly available, fully sequenced bacterial genomes (n = 2522) and plasmids (n = 4582). The only BMRGs commonly co-occurring with ARGs on plasmids were mercury resistance genes and the qacE∆1 gene that provides low-level resistance to quaternary ammonium compounds. Novel connections between cadmium/zinc and macrolide/aminoglycoside resistance genes were also uncovered. Several clinically important bacterial taxa were particularly prone to carry both BMRGs and ARGs. Bacteria carrying BMRGs more often carried ARGs compared to bacteria without (p < 0.0001). BMRGs were found in 86 % of bacterial genomes, and co-occurred with ARGs in 17 % of the cases. In contrast, co-occurrences of BMRGs and ARGs were rare on plasmids from all external environments (<0.7 %) but more common on those of human and domestic animal origin (5 % and 7 %, respectively). Finally, plasmids with both BMRGs and ARGs were more likely to be conjugative (p < 0.0001) and carry toxin-antitoxin systems (p < 0.0001) than plasmids without resistance genes.

CONCLUSIONS

This is the first large-scale identification of compounds, taxa and environments of particular concern for co-selection of resistance against antibiotics, biocides and metals. Genetic co-occurrences suggest that plasmids provide limited opportunities for biocides and metals to promote horizontal transfer of antibiotic resistance through co-selection, whereas ample possibilities exist for indirect selection via chromosomal BMRGs. Taken together, the derived patterns improve our understanding of co-selection potential between biocides, metals and antibiotics, and thereby provide guidance for risk-reducing actions.

Co-occurrence of resistance genes to antibiotics, biocides and metals reveals novel insights into their co-selection potential

BACKGROUND

Antibacterial biocides and metals can co-select for antibiotic resistance when bacteria harbour resistance or tolerance genes towards both types of compounds. Despite numerous case studies, systematic and quantitative data on co-occurrence of such genes on plasmids and chromosomes is lacking, as is knowledge on environments and bacterial taxa that tend to carry resistance genes to such compounds. This effectively prevents identification of risk scenarios. Therefore, we aimed to identify general patterns for which biocide/metal resistance genes (BMRGs) and antibiotic resistance genes (ARGs) that tend to occur together. We also aimed to quantify co-occurrence of resistance genes in different environments and taxa, and investigate to what extent plasmids carrying both types of genes are conjugative and/or are carrying toxin-antitoxin systems.

RESULTS

Co-occurrence patterns of resistance genes were derived from publicly available, fully sequenced bacterial genomes (n = 2522) and plasmids (n = 4582). The only BMRGs commonly co-occurring with ARGs on plasmids were mercury resistance genes and the qacE∆1 gene that provides low-level resistance to quaternary ammonium compounds. Novel connections between cadmium/zinc and macrolide/aminoglycoside resistance genes were also uncovered. Several clinically important bacterial taxa were particularly prone to carry both BMRGs and ARGs. Bacteria carrying BMRGs more often carried ARGs compared to bacteria without (p < 0.0001). BMRGs were found in 86 % of bacterial genomes, and co-occurred with ARGs in 17 % of the cases. In contrast, co-occurrences of BMRGs and ARGs were rare on plasmids from all external environments (<0.7 %) but more common on those of human and domestic animal origin (5 % and 7 %, respectively). Finally, plasmids with both BMRGs and ARGs were more likely to be conjugative (p < 0.0001) and carry toxin-antitoxin systems (p < 0.0001) than plasmids without resistance genes.

CONCLUSIONS

This is the first large-scale identification of compounds, taxa and environments of particular concern for co-selection of resistance against antibiotics, biocides and metals. Genetic co-occurrences suggest that plasmids provide limited opportunities for biocides and metals to promote horizontal transfer of antibiotic resistance through co-selection, whereas ample possibilities exist for indirect selection via chromosomal BMRGs. Taken together, the derived patterns improve our understanding of co-selection potential between biocides, metals and antibiotics, and thereby provide guidance for risk-reducing actions.

Assessing variation in the potential susceptibility of fish to pharmaceuticals, considering evolutionary differences in their physiology and ecology

Fish represent the planet's most diverse group of vertebrates and they can be exposed to a wide range of pharmaceuticals. For practical reasons, extrapolation of pharmaceutical effects from 'model' species to other fish species is adopted in risk assessment. Here, we critically assess this approach. First, we show that between 65% and 86% of human drug targets are evolutionarily conserved in 12 diverse fish species. Focusing on nuclear steroid hormone receptors, we further show that the sequence of the ligand binding domain that plays a key role in drug potency is highly conserved, but there is variation between species. This variation for the oestrogen receptor, however, does not obviously account for observed differences in receptor activation. Taking the synthetic oestrogen ethinyloestradiol as a test case, and using life-table-response experiments, we demonstrate significant reductions in population growth in fathead minnow and medaka, but not zebrafish, for environmentally relevant exposures. This finding contrasts with zebrafish being ranked as more ecologically susceptible, according to two independent life-history analyses. We conclude that while most drug targets are conserved in fish, evolutionary divergence in drug-target activation, physiology, behaviour and ecological life history make it difficult to predict population-level effects. This justifies the conventional use of at least a 10× assessment factor in pharmaceutical risk assessment, to account for differences in species susceptibility.

Metagenomic sequencing of marine periphyton: taxonomic and functional insights into biofilm communities

Periphyton communities are complex phototrophic, multispecies biofilms that develop on surfaces in aquatic environments. These communities harbor a large diversity of organisms comprising viruses, bacteria, algae, fungi, protozoans, and metazoans. However, thus far the total biodiversity of periphyton has not been described. In this study, we use metagenomics to characterize periphyton communities from the marine environment of the Swedish west coast. Although we found approximately ten times more eukaryotic rRNA marker gene sequences compared to prokaryotic, the whole metagenome-based similarity searches showed that bacteria constitute the most abundant phyla in these biofilms. We show that marine periphyton encompass a range of heterotrophic and phototrophic organisms. Heterotrophic bacteria, including the majority of proteobacterial clades and Bacteroidetes, and eukaryotic macro-invertebrates were found to dominate periphyton. The phototrophic groups comprise Cyanobacteria and the alpha-proteobacterial genus Roseobacter, followed by different micro- and macro-algae. We also assess the metabolic pathways that predispose these communities to an attached lifestyle. Functional indicators of the biofilm form of life in periphyton involve genes coding for enzymes that catalyze the production and degradation of extracellular polymeric substances, mainly in the form of complex sugars such as starch and glycogen-like meshes together with chitin. Genes for 278 different transporter proteins were detected in the metagenome, constituting the most abundant protein complexes. Finally, genes encoding enzymes that participate in anaerobic pathways, such as denitrification and methanogenesis, were detected suggesting the presence of anaerobic or low-oxygen micro-zones within the biofilms.

Quinolone resistance mutations in the faecal microbiota of Swedish travellers to India

Background

International travel contributes to the spread of antibiotic resistant bacteria over the world. Most studies addressing travel-related changes in the faecal flora have focused on specific mobile resistance genes, or depended on culturing of individual bacterial isolates. Antibiotic resistance can, however, also spread via travellers colonized by bacteria carrying chromosomal antibiotic resistance mutations, but this has received little attention so far. Here we aimed at exploring the abundance of chromosomal quinolone resistance mutations in Escherichia communities residing in the gut of Swedish travellers, and to determine potential changes after visiting India. Sweden is a country with a comparably low degree of quinolone use and quinolone resistance, whereas the opposite is true for India.

Methods

Massively parallel amplicon sequencing targeting the quinolone-resistance determining region of gyrA and parC was applied to total DNA extracted from faecal samples. Paired samples were collected from 12 Swedish medical students before and after a 4–15 week visit to India. Twelve Indian residents were included for additional comparisons. Methods known resistance mutations were common in Swedes before travel as well as in Indians, with a trend for all mutations to be more common in the Indian sub group. There was a significant increase in the abundance of the most common amino acid substitution in GyrA (S83L, from 44 to 72 %, p = 0.036) in the samples collected after return to Sweden. No other substitution, including others commonly associated with quinolone resistance (D87N in GyrA, S80I in ParC) changed significantly. The number of distinct genotypes encoded in each traveller was significantly reduced after their visit to India for both GyrA (p = 0.0020) and ParC (p = 0.0051), indicating a reduced genetic diversity, similar to that found in the Indians.

Conclusions

International travel can alter the composition of the Escherichia communities in the faecal flora, favouring bacteria carrying certain resistance mutations, and, thereby, contributes to the global spread of antibiotic resistance. A high abundance of specific mutations in Swedish travellers before visiting India is consistent with the hypothesis that these mutation have no fitness cost even in the absence of an antibiotic selection pressure.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-015-0574-6) contains supplementary material, which is available to authorized users.