Fredrik Boulund

Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India

There is increasing evidence for an environmental origin of many antibiotic resistance genes. Consequently, it is important to identify environments of particular risk for selecting and maintaining such resistance factors. In this study, we described the diversity of antibiotic resistance genes in an Indian lake subjected to industrial pollution with fluoroquinolone antibiotics. We also assessed the genetic context of the identified resistance genes, to try to predict their genetic transferability. The lake harbored a wide range of resistance genes (81 identified gene types) against essentially every major class of antibiotics, as well as genes responsible for mobilization of genetic material. Resistance genes were estimated to be 7000 times more abundant than in a Swedish lake included for comparison, where only eight resistance genes were found. The sul2 and qnrD genes were the most common resistance genes in the Indian lake. Twenty-six known and 21 putative novel plasmids were recovered in the Indian lake metagenome, which, together with the genes found, indicate a large potential for horizontal gene transfer through conjugation. Interestingly, the microbial community of the lake still included a wide range of taxa, suggesting that, across most phyla, bacteria has adapted relatively well to this highly polluted environment. Based on the wide range and high abundance of known resistance factors we have detected, it is plausible that yet unrecognized resistance genes are also present in the lake. Thus, we conclude that environments polluted with waste from antibiotic manufacturing could be important reservoirs for mobile antibiotic resistance genes.

A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences

BackgroundBroad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail.ResultsIn this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature.ConclusionsThe method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/.

In vitro assessment of platelet concentrates with multiple electrode aggregometry

ABSTRACT Storage impairs platelet function. It was hypothesized that multiple electrode aggregometry in vitro could be used to follow aggregability in platelet concentrates over time and that the results predict the efficacy of platelet transfusion in an ex vivo transfusion model. In vitro platelet aggregability was assessed in apheresis and pooled buffy coat platelet concentrates (BCs) (n = 13 each) using multiple electrode aggregometry with different agonists 1, 3, 5 and 7 days after preparation. In the ex vivo transfusion model, whole blood samples from nine healthy volunteers were collected every second day. The samples were supplemented with stored platelets (+146 × 109 × l−1) from the same unit 1, 3, 5 and 7 days after preparation. Platelet aggregability was assessed in the concentrate and in the whole blood samples before and after platelet supplementation. There was a continuous reduction in in vitro platelet aggregability over time in both apheresis and pooled BCs. The same pattern was observed after ex vivo addition of apheresis and pooled BCs to whole blood samples. The best correlation between in vitro aggregability and changes in aggregation after addition was achieved with collagen as agonist (r = 0.67, p < 0.001). In conclusion, multiple electrode aggregometry can be used to follow aggregability in platelet concentrates in vitro, and the results predict with moderate accuracy changes in aggregation after addition of platelet concentrate to whole blood samples.

Functional verification of computationally predicted qnr genes

BackgroundThe quinolone resistance (qnr) genes are widely distributed among bacteria. We recently developed and applied probabilistic models to identify tentative novel qnr genes in large public collections of DNA sequence data including fragmented metagenomes.FindingsBy using inducible recombinant expressions systems the functionality of four identified qnr candidates were evaluated in Escherichia coli. Expression of several known qnr genes as well as two novel candidates provided fluoroquinolone resistance that increased with elevated inducer concentrations. The two novel, functionally verified qnr genes are termed Vfuqnr and assembled qnr 1. Co-expression of two qnr genes suggested non-synergistic action.ConclusionThe combination of a computational model and recombinant expression systems provides opportunities to explore and identify novel antibiotic resistance genes in both genomic and metagenomic datasets.

Strategies to improve usability and preserve accuracy in biological sequence databases

Biology is increasingly dependent on large‐scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high‐quality, informative annotation from less useful ones; (iv) to integrate automated quality‐control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large‐scale data.

Tentacle: distributed quantification of genes in metagenomes.

BackgroundIn metagenomics, microbial communities are sequenced at increasingly high resolution, generating datasets with billions of DNA fragments. Novel methods that can efficiently process the growing volumes of sequence data are necessary for the accurate analysis and interpretation of existing and upcoming metagenomes.FindingsHere we present Tentacle, which is a novel framework that uses distributed computational resources for gene quantification in metagenomes. Tentacle is implemented using a dynamic master-worker approach in which DNA fragments are streamed via a network and processed in parallel on worker nodes. Tentacle is modular, extensible, and comes with support for six commonly used sequence aligners. It is easy to adapt Tentacle to different applications in metagenomics and easy to integrate into existing workflows.ConclusionsEvaluations show that Tentacle scales very well with increasing computing resources. We illustrate the versatility of Tentacle on three different use cases. Tentacle is written for Linux in Python 2.7 and is published as open source under the GNU General Public License (v3). Documentation, tutorials, installation instructions, and the source code are freely available online at: http://bioinformatics.math.chalmers.se/tentacle.

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.

Computational and Statistical Considerations in the Analysis of Metagenomic Data

In shotgun metagenomics, microbial communities are studied by random DNA fragments sequenced directly from environmental and clinical samples. The resulting data is massive, potentially consisting of billions of sequence reads describing millions of microbial genes. The data interpretation is therefore nontrivial and dependent on dedicated computational and statistical methods. In this chapter we discuss the many challenges associated with the analysis of shotgun metagenomic data. First, we address computational issues related to the quantification of genes in metagenomes. We describe algorithms for efficient sequence comparisons, recommended practices for setting up data workflows and modern high-performance computer resources that can be used to perform the analysis. Next, we outline the statistical aspects, including removal of systematic errors and how to identify differences between microbial communities from different experimental conditions. We conclude by underlining the increasing importance of efficient and reliable computational and statistical solutions in the analysis of large metagenomic datasets.

Draft Genome Sequence of Extended-Spectrum-β-Lactamase-Producing Escherichia coli Strain CCUG 62462, Isolated from a Urine Sample

ABSTRACT The draft genome sequence has been determined for an extended-spectrum-β-lactamase (ESBL)-producing (blaCTX-M-15) Escherichia coli strain (CCUG 62462), composed of 119 contigs and a total size of 5.27 Mb. This E. coli is serotype O25b and sequence type 131, a pandemic clonal group, causing worldwide antimicrobial-resistant infections.

Draft Genome Sequence of Streptococcus gordonii Type Strain CCUG 33482T

ABSTRACT Streptococcus gordonii type strain CCUG 33482T is a member of the Streptococcus mitis group, isolated from a case of subacute bacterial endocarditis. Here, we report the draft genome sequence of S. gordonii CCUG 33482T, composed of 41 contigs of a total size of 2.15 Mb with 2,061 annotated coding sequences.