| 1. |
- Berglund, Fanny, 1988, et al.
(author)
-
Identification and reconstruction of novel antibiotic resistance genes from metagenomes
- 2019
-
In: Microbiome. - : Springer Science and Business Media LLC. - 2049-2618. ; 7:1
-
Journal article (peer-reviewed)abstract
- BackgroundEnvironmental and commensal bacteria maintain a diverse and largely unknown collection of antibiotic resistance genes (ARGs) that, over time, may be mobilized and transferred to pathogens. Metagenomics enables cultivation-independent characterization of bacterial communities but the resulting data is noisy and highly fragmented, severely hampering the identification of previously undescribed ARGs. We have therefore developed fARGene, a method for identification and reconstruction of ARGs directly from shotgun metagenomic data.ResultsfARGene uses optimized gene models and can therefore with high accuracy identify previously uncharacterized resistance genes, even if their sequence similarity to known ARGs is low. By performing the analysis directly on the metagenomic fragments, fARGene also circumvents the need for a high-quality assembly. To demonstrate the applicability of fARGene, we reconstructed -lactamases from five billion metagenomic reads, resulting in 221 ARGs, of which 58 were previously not reported. Based on 38 ARGs reconstructed by fARGene, experimental verification showed that 81% provided a resistance phenotype in Escherichia coli. Compared to other methods for detecting ARGs in metagenomic data, fARGene has superior sensitivity and the ability to reconstruct previously unknown genes directly from the sequence reads.ConclusionsWe conclude that fARGene provides an efficient and reliable way to explore the unknown resistome in bacterial communities. The method is applicable to any type of ARGs and is freely available via GitHub under the MIT license.
|
|
| 2. |
- Berglund, Fanny, 1988, et al.
(author)
-
Identification of 76 novel B1 metallo-beta-lactamases through large-scale screening of genomic and metagenomic data
- 2017
-
In: Microbiome. - : Springer Science and Business Media LLC. - 2049-2618. ; 5:1, s. 134-134
-
Journal article (peer-reviewed)abstract
- Background: Metallo-beta-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-beta-lactamases, some of which may spread to pathogens, thereby threatening public health. The aim of this study was to identify novel metallo-beta-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-beta-lactamase genes. In total, 76 novel genes were predicted, forming 59 previously undescribed metallo-beta-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-beta-lactamase genes contained atypical zinc-binding motifs in their active sites, which were previously undescribed for metallo-beta-lactamases. Phylogenetic analysis showed that B1 metallo-beta-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 beta-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-beta-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.
|
|
| 3. |
- Corcoll, Natàlia, 1981, et al.
(author)
-
Comparison of four DNA extraction methods for comprehensive assessment of 16S rRNA bacterial diversity in marine biofilms using high-throughput sequencing
- 2017
-
In: FEMS Microbiology Letters. - : Oxford University Press (OUP). - 1574-6968 .- 0378-1097. ; 364:14
-
Journal article (peer-reviewed)abstract
- High-throughput DNA sequencing technologies are increasingly used for the metagenomic characterization of microbial biodiversity. However, basic issues, such as the choice of an appropriate DNA extraction method, are still not resolved for non-model microbial communities. This study evaluates four commonly used DNA extraction methods for marine periphyton biofilms in terms of DNA yield, efficiency, purity, integrity and resulting 16S rRNA bacterial diversity. Among the tested methods, the Plant DNAzol® Reagent (PlantDNAzol) and the Fast DNATM SPIN Kit for Soil (FastDNA Soil) methods were best suited to extract high quantities of DNA (77 - 130 μg g wet wt-1). Lower amounts of DNA were obtained (
|
|
| 4. |
- Förlin, Lars, 1950, et al.
(author)
-
mRNA Expression and Biomarker Responses in Perch at a Biomonitoring Site in the Baltic Sea - Possible Influence of Natural Brominated Chemicals
- 2019
-
In: Frontiers in Marine Science. - : Frontiers Media SA. - 2296-7745. ; 6
-
Journal article (peer-reviewed)abstract
- Perch (Perca fluviatilis) has been used in biological effect monitoring in a program for integrated coastal fish monitoring at the reference site Kvadofjarden along the Swedish east coast, which is a site characterized by no or minor local anthropogenic influences. Using a set of physiological and biochemical endpoints (i.e., biomarkers), clear time trends for "early warning" signs of impaired health were noted in the perch from this site, possibly as a result of increased baseline pollution. The data sets also showed relatively large variations among years. To identify additional temporal variation in biological parameters, global mRNA expression studies using RNA sequencing was performed. Perch collected in 2010 and 2014 were selected, as they showed variations in several biomarkers, such as the activity of the detoxification enzyme CYP1A (EROD), the plasma levels of vitellogenin, markers for oxidative stress, white blood cells count and gonad sizes. The RNA sequencing study identified approximately 4800 genes with a significantly difference in mRNA expression levels. A gene ontology enrichment analysis showed that these differentially expressed genes were involved in biological processes such as complement activation, iron ion homeostasis and cholesterol biosynthetic process. In addition, differences in immune system parameters and responses to the exposure of toxic substances have now been verified in two different biological levels (mRNA and protein) in perch collected in 2010 and 2014. Markedly higher mRNA expression of the membrane transporter (MATE) and the detoxification enzyme COMT, together with higher concentrations of bioactive naturally produced brominated compounds, such as brominated indoles and carbazoles, seem to indicate that the perch collected in 2014 had been exposed to macro- and microalga blooming to a higher degree than did perch from 2010. These results and the differential mRNA expression between the 2 years in genes related to immune and oxidative stress parameters suggest that attention must be given to algae blooming when elucidating the well-being of the perch at Kvadofjarden and other Baltic coastal sites.
|
|
| 5. |
- Gunnarsson, L., et al.
(author)
-
Pharmacology beyond the patient - The environmental risks of human drugs
- 2019
-
In: Environment International. - : Elsevier BV. - 0160-4120 .- 1873-6750. ; 129, s. 320-332
-
Journal article (peer-reviewed)abstract
- Background: The presence of pharmaceuticals in the environment is a growing global concern and although environmental risk assessment is required for approval of new drugs in Europe and the USA, the adequacy of the current triggers and the effects-based assessments has been questioned. Objective: To provide a comprehensive analysis of all regulatory compliant aquatic ecotoxicity data and evaluate the current triggers and effects-based environmental assessments to facilitate the development of more efficient approaches for pharmaceuticals toxicity testing. Methods: Publicly-available regulatory compliant ecotoxicity data for drugs targeting human proteins was compiled together with pharmacological information including drug targets, Cmax and lipophilicity. Possible links between these factors and the ecotoxicity data for effects on, growth, mortality and/or reproduction, were evaluated. The environmental risks were then assessed based on a combined analysis of drug toxicity and predicted environmental concentrations based on European patient consumption data. Results: For most (88%) of the of 975 approved small molecule drugs targeting human proteins a complete set of regulatory compliant ecotoxicity data in the public domain was lacking, highlighting the need for both intelligent approaches to prioritize legacy human drugs for a tailored environmental risk assessment and a transparent database that captures environmental data. We show that presence/absence of drug-target orthologues are predictive of susceptible species for the more potent drugs. Drugs that target the endocrine system represent the highest potency and greatest risk. However, for most drugs ( > 80%) with a full set of ecotoxicity data, risk quotients assuming worst-case exposure assessments were below one in all European countries indicating low environmental risks for the endpoints assessed. Conclusion: We believe that the presented analysis can guide improvements to current testing procedures, and provide valuable approaches for prioritising legacy drugs (i.e. those registered before 2006) for further ecotoxicity testing. For drugs where effects of possible concern (e.g. behaviour) are not captured in regulatory tests, additional mechanistic testing may be required to provide the highest confidence for avoiding environmental impacts.
|
|
| 6. |
- Heß, Stefanie, et al.
(author)
-
Sewage from Airplanes Exhibits High Abundance and Diversity of Antibiotic Resistance Genes
- 2019
-
In: Environmental Science & Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 53:23, s. 13898-13905
-
Journal article (peer-reviewed)abstract
- Airplane sanitary facilities are shared by an international audience. We hypothesized the corresponding sewage to be an extraordinary source of antibiotic-resistant bacteria (ARB) and resistance genes (ARG) in terms of diversity and quantity. Accordingly, we analyzed ARG and ARB in airplane-borne sewage using complementary approaches: Metagenomics, quantitative polymerase chain reaction (qPCR), and cultivation. For the purpose of comparison, we also quantified ARG and ARB in the inlets of municipal treatment plants with and without connection to airports. As expected, airplane sewage contained an extraordinarily rich set of mobile ARG, and the relative abundances of genes were mostly increased compared to typical raw sewage of municipal origin. Moreover, combined resistance against third-generation cephalosporins, fluorochinolones, and aminoglycosides was unusually common (28.9%) among Escherichia coli isolated from airplane sewage. This percentage exceeds the one reported for German clinical isolates by a factor of 8. Our findings suggest that airplane-borne sewage can effectively contribute to the fast and global spread of antibiotic resistance.
|
|
| 7. |
- Jonsson, Viktor, 1987, et al.
(author)
-
Modelling of zero-inflation improves inference of metagenomic gene count data
- 2019
-
In: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 28:12, s. 3712-3728
-
Journal article (peer-reviewed)abstract
- Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data. By analysis of three comprehensive datasets, we show that zero-inflation is common in metagenomic data from the human gut and, if not correctly modelled, it can lead to substantial reductions in statistical power. We also show, by using resampled metagenomic data, that our model has, compared to other methods, a higher and more stable performance for detecting differentially abundant genes. We conclude that proper modelling of the gene-specific variability, including the excess of zeros, is necessary to accurately describe gene abundances in metagenomic data. The proposed model will thus pave the way for new biological insights into the structure of microbial communities.
|
|
| 8. |
- Jonsson, Viktor, 1987, et al.
(author)
-
Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics
- 2016
-
In: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 17
-
Journal article (peer-reviewed)abstract
- 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.
|
|
| 9. |
- Jonsson, Viktor, 1987, et al.
(author)
-
Variability in Metagenomic Count Data and Its Influence on the Identification of Differentially Abundant Genes.
- 2017
-
In: Journal of Computational Biology. - : Mary Ann Liebert Inc. - 1066-5277 .- 1557-8666. ; 24:4, s. 311-326
-
Journal article (peer-reviewed)abstract
- 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.
|
|
| 10. |
- Verbruggen, B., et al.
(author)
-
ECOdrug: A database connecting drugs and conservation of their targets across species
- 2018
-
In: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 46:D1
-
Journal article (peer-reviewed)abstract
- Pharmaceuticals are designed to interact with specific molecular targets in humans and these targets generally have orthologs in other species. This provides opportunities for the drug discovery community to use alternative model species for drug development. It also means, however, there is potential for mode of action related effects in non-target wildlife species as many pharmaceuticals reach the environment through patient use and manufacturing wastes. Acquiring insight in drug target ortholog predictions across species and taxonomic groups has proven difficult because of the lack of an optimal strategy and because necessary information is spread across multiple and diverse sources and platforms. We introduce a new research platform tool, ECOdrug, that reliably connects drugs to their protein targets across divergent species. It harmonizes ortholog predictions from multiple sources via a simple user interface underpinning critical applications for a wide range of studies in pharmacology, ecotoxicology and comparative evolutionary biology. ECOdrug can be used to identify species with drug targets and identify drugs that interact with those targets. As such, it can be applied to support intelligent targeted drug safety testing by ensuring appropriate and relevant species are selected in ecological risk assessments. ECOdrug is freely accessible and available at: Http://www.ecodrug.org. © 2017 The Author(s).
|
|
