| 1. |
- Bengtsson-Palme, Johan, 1985, et al.
(författare)
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Strategies to improve usability and preserve accuracy in biological sequence databases
- 2016
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 16:18, s. 2454-2460
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Tidskriftsartikel (refereegranskat)abstract
- 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 identifications 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 post-submission 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.
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| 2. |
- Boulund, Fredrik, 1985, et al.
(författare)
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Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets
- 2017
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 18:1, s. Art 682-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Andersson Shams Hakimi, Caroline, et al.
(författare)
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In vitro assessment of platelet concentrates with multiple electrode aggregometry.
- 2015
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Ingår i: Platelets. - : Informa UK Limited. - 0953-7104 .- 1369-1635. ; 26:2, s. 132-137
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Tidskriftsartikel (refereegranskat)abstract
- 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 × 10(9) × 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
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| 4. |
- Berglund, Fanny, 1988, et al.
(författare)
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Identification and reconstruction of novel antibiotic resistance genes from metagenomes
- 2019
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Ingår i: Microbiome. - : Springer Science and Business Media LLC. - 2049-2618. ; 7:1
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Tidskriftsartikel (refereegranskat)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.
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| 5. |
- Boulund, Fredrik, et al.
(författare)
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Computational and Statistical Considerations in the Analysis of Metagenomic Data
- 2018
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Ingår i: Metagenomics: Perspectives, Methods, and Applications. - 9780081022689 ; , s. 81-102
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Boulund, Fredrik, 1985
(författare)
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Computational methods for analysis of fragmented sequence data
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recent developments in genomic and proteomic sequencing technologies have revolutionized research in life sciences, providing new opportunities for the study of biological systems. However, modern sequence data sets are large, diverse, and heavily fragmented, which presents new challenges for their analysis and interpretation. In this thesis we present six research papers, that describe novel methods for studying bacteria and bacterial communities through the analysis of large data sets produced by modern DNA and protein sequencing technologies. In Paper I, we describe a method for discovering fragments of fluoroquinolone antibiotic resistance genes in short fragments of DNA. The resistance phenotypes of the predicted resistance genes were then validated by expression in an Escherichia coli host (Paper II). The method was further improved to handle larger and more fragmented data sets in Paper III. In Paper IV, we present Tentacle, an easy-to-use tool for high performance gene quantification in metagenomes that can be run on distributed computing resources to enable fast and efficient gene quantification in terabase metagenomes. In Paper V, we introduce proteotyping, an approach for microbial identification in clinical samples based on shotgun proteomics. Finally, in Paper VI we describe and evaluate a method for proteotyping analysis suited for application to clinical diagnostics of bacterial infections. The rapidly increasing volumes of data produced by new sequencing technologies provide new opportunities for understanding microbial biology. To unlock the full potential of large sequence data sets requires novel methods and approaches such as those presented in this thesis.
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| 7. |
- Boulund, Fredrik, 1985, et al.
(författare)
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Tentacle: distributed quantification of genes in metagenomes
- 2015
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Ingår i: GigaScience. - : Oxford University Press (OUP). - 2047-217X .- 2047-217X. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Background In 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. Findings Here 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. Conclusions Evaluations 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.
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| 8. |
- Boulund, Fredrik, 1985, et al.
(författare)
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Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics
- 2017
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Ingår i: Molecular & Cellular Proteomics. - 1535-9476 .- 1535-9484. ; 16:6, s. 1052-1063
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Tidskriftsartikel (refereegranskat)abstract
- 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 beta-lactamases in an extended spectrum beta-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.
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| 9. |
- Gonzales-Siles, Lucia, et al.
(författare)
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Mass Spectrometry Proteotyping for detection, identification characterization and diagnostics of infectious bacteria in clinical respiratory-tract samples
- 2016
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Ingår i: 11th International Meeting on Microbial Epidemiological Markers (IMMEM XI) 9 - 12 March 2016, Estoril, Portugal.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background. Lower respiratory tract infection (LRTI) is the leading cause of childhood deaths in most developing countries and the world (?) and are the most common causes of hospital and out-patient visits within the EU, comprising 1 of 3 admissions annually. In general, the over-prescription and use of broad-spectrum antibiotics are common practices that lead to the evolution and development of resistance in infectious bacteria and will lead to loss of time and resources in patient handling and adverse patient outcomes. Conventional approaches have depended upon cultivation of bacteria with subsequent testing for antibiotic sensitivity. Therefore, reliable and time-effective microbiological diagnostics are essential for more effective treatment of respiratory infections. In this project, we apply state-of-the-art proteomics techniques for identifications of pathogens and antibiotic resistance from clinical samples, without prior cultivation. Material and methods. Nasopharyngeal swab samples were collected, in commercial Amies medium supplemented with 5x STGG, as a part of the EU-TAILORED-Treatment project (www.tailored-treatment.eu/). Samples were stored at -20°C until analyses. Different protocols for removal of human cells and mucus were tested, including non-ionic detergents, i.e., Igepal, Saponin, Urea-Chaps, as well as cytolysis. Samples were concentrated and analyzed by ‘proteotyping’ (1), using a Lipid-based Protein Immobilization (LPITM) technology (WO2006068619), in which intact bacterial cells or cell fractions are bound to a surface. Peptides were generated, using enzymatic digestion, and then separated and analyzed, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The mass spectra profiles were compared to a database of reference peptide sequences, consisting of all complete genomes of the NCBI Reference Sequence (RefSeq) Database. Results were confirmed by standard microbiology, including cultivation of bacteria in selective media, MALDI-TOF MS analyses and qPCR. Results. Proteotyping applied to clinical samples demonstrated that the number of viable bacteria and detected proteins determined were ten-times higher when nasal swabs were stored in Amies media supplemented with STGG 5X media compared to Amies media without STGG, after 1 and 2 months of storage at -70C. Among the different protocols tested to remove human biomaterial, all treatments proved effective to varying degrees, although the Igepal treatment was able to retain the highest number of discriminatory peptides. Using proteotyping, we were able to identify the pathogenic bacteria directly within clinical samples (nasopharyngeal and nasal swabs) that had been identified to be positive for respiratory infectious bacteria by standard methodologies at clinical bacteriology laboratories at Sahlgrenska University Hospital (Sweden) or Universitair Medisch Centrum Utrecht (Netherlands). Conclusions. Proteotyping of infectious bacteria, using tandem LC-MS/MS enabled the differentiation and identification of infectious bacteria in clinical samples from LRTIs. It has high levels of resolution and highly reproducible detection of protein biomarkers. Proteotyping identified biomarkers for species- and sub-species-level strain discrimination and antibiotic resistance, all from single MS analyses. 1) Karlsson et al., 2015. Syst. Appl. Microbiol. 38 :246-257.
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| 10. |
- Gonzales-Siles, Lucia, et al.
(författare)
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Mass Spectrometry Proteotyping of Streptococcus pneumoniae and commensal Streptococcus: identification of biomarkers for infectious strain characterization
- 2016
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Ingår i: 26th ECCMID 2016 Amsterdam, The Netherlands. 9 - 12 April 2016.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background: Streptococcus pneumoniae (pneumococcus) is the leading cause of community-acquired pneumonia, with morbidity and mortality worldwide. S. pneumoniae belongs to the S. mitis-Group (viridans streptococci), phenotypically and genotypically similar to commensal species of the upper respiratory tract, S. mitis, S. oralis, and S. pseudopneumoniae, causing problems for identifications in clinical laboratories. In this project, we apply state-of-the-art proteomics for Streptococcus spp. 'proteotyping'; identifying and characterizing protein biomarkers for species-level identification, antibiotic resistance, virulence and strain typing for epidemiological analyses (1). Material/methods: Bacterial proteins, from intact bacteria or cell fractions, are bound to a membrane surface, using patented (WO2006068619) FlowCell (LPITM) technology. Peptides are generated from the bound proteins, by enzymatic digestion, separated and analyzed, using LC-MS/MS. The mass spectra profiles are compared to reference peptide sequences and whole genome sequence (wgs) data of the NCBI RefSeq Database. The S. mitis-Group specie, S. pneumoniae, S. mitis, S. oralis, S. psedopneumoniae, as well as the more distantly-related, Group A Streptococcus (GAS) species, S. pyogenes , were analyzed individually and in mixtures, to demonstrate the resolution of proteotyping for differentiating bacteria. Results: Using proteotyping protocols, S. pneumoniae were detected and differentiated from other streptococci, S. mitis, S. oralis, S. psedopneumoniae and the more distant relative, S. pyogenes, by identification of unique discriminatory peptides. Metabolic protein biomarkers were identified, including for antibiotic resistance and virulence. It was possible to find discriminatory biomarkers for a target species when analyzing 1:1 mixes of S. pneumoniae and other species from the S. mitis-Group. The different strains of S. pneumoniae, analyzed in different ratio combinations, were successfully differentiated and identified. For successful proteotyping, a comprehensive and accurate genomic database was observed to be key for obtaining reliable peptide matching and proteotyping data. Importantly, because of observed high rates of misclassified wgs data in the public databases, the taxonomic classifications of genomes in GenBank were analyzed against reference type strain genomes of target species by calculating wgs similarities, using Average Nucleotide Identity with BLAST (ANIb). While wgs data for S. pneumoniae were confirmed to be classified correctly, approximately one-third of wgs data for other species of the S. mitis-Group were determined to be misclassified. Streptococci strains that could not be identified, using standard genotypic and phenotypic approaches, were characterized by proteotyping and genome sequencing to establish their taxonomy and biomarker features to enhance species database matching. Conclusions: Proteotyping enables differentiation, identification and characterization of pneumococcus from the most closely related species attaining, as well, strain-level discrimination from single LC-MS/MS analyses. The protocol enhances identification and characterization of pathogenic bacterial isolates through identifications of expressed biomarkers, ultimately for cultivation-independent analyses of clinical samples. 1) Karlsson et al., 2015. Syst Appl Microbiol. 38:246-257.
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