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2. Contaminating viral sequences in high-throughput sequencing viromics: a linkage study of 700 sequencing libraries

Author

Asplund, M., Kjartansdóttir, K.R., Mollerup, S., Vinner, L., Fridholm, H., Herrera, J.A.R., Friis-Nielsen, J., Hansen, T.A., Jensen, R.H., Nielsen, I.B., Richter, S.R., Rey-Iglesia, A., Matey-Hernandez, M.L., Alquezar-Planas, D.E., Olsen, P.V.S., Sicheritz-Pontén, T., Willerslev, E., Lund, O., Brunak, S., Mourier, T., Nielsen, L.P., Izarzugaza, J.M.G., and Hansen, A.J.

Published
2019
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7. The Immune Epitope Database and Analysis Resource

Author

Sette, A., Bui, H. H., Sidney, J., Bourne, P., Buus, S., Fleri, W., Kubo, R., Lund, O., Nemazee, D., Ponomarenko, J. V., Sathiamurthy, M., Stewart, S., Way, S., Wilson, S. S., Peters, B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Rajapakse, Jagath C., editor, Wong, Limsoon, editor, and Acharya, Raj, editor

Published
2006
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8. Acid Deposition and Acidification of Soil and Water in the Tie Shan Ping Area, Chongqing, China

Author

Dawei, Zhao, Larssen, T., Dongbao, Zhang, Shidong, Gao, Vogt, R. D., Seip, H. M., Lund, O. J., Satake, Kenichi, editor, Shindo, Junko, editor, Takamatsu, Takejiro, editor, Nakano, Takanori, editor, Aoki, Shigeru, editor, Fukuyama, Tsutomu, editor, Hatakeyama, Shiro, editor, Ikuta, Kazukamasa, editor, Kawashima, Munetsugu, editor, Kohno, Yoshihisa, editor, Kojima, Satoru, editor, Murano, Kentaro, editor, Okita, Toshiichi, editor, Taoda, Hiroshi, editor, Tsunoda, Kinichi, editor, and Tsurumi, Makoto, editor

Published
2001
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14. Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

Author

Pataki B. A., Matamoros S., van der Putten B. C. L., Remondini D., Giampieri E., Aytan-Aktug D., Hendriksen R. S., Lund O., Csabai I., Schultsz C., Janes V., Clausen P., Aarestrup F. M., Koopmans M., Pataki B., Visontai D., Steger J., Szalai-Gindl J. M., Pakseresht N., Rossello M., Silvester N., Amid C., Cochrane G., Pradel F., Westeel E., Fuchs S., Kumar S. M., Xavier B. B., Ngoc M. N., Pasquali F., Petrovska L., Ajayi D., Nielsen E. M., Trung N. V., Hoa N. T., Ishii Y., Aoki K., McDermott P., Virology, Pataki B.A., Matamoros S., van der Putten B.C.L., Remondini D., Giampieri E., Aytan-Aktug D., Hendriksen R.S., Lund O., Csabai I., Schultsz C., Janes V., Clausen P., Aarestrup F.M., Koopmans M., Pataki B., Visontai D., Steger J., Szalai-Gindl J.M., Pakseresht N., Rossello M., Silvester N., Amid C., Cochrane G., Pradel F., Westeel E., Fuchs S., Kumar S.M., Xavier B.B., Ngoc M.N., Pasquali F., Petrovska L., Ajayi D., Nielsen E.M., Trung N.V., Hoa N.T., Ishii Y., Aoki K., McDermott P., Medical Microbiology and Infection Prevention, Global Health, AII - Infectious diseases, Graduate School, and APH - Global Health

Subjects
0301 basic medicine, Antibiotics, lcsh:Medicine, computer.software_genre, medicine.disease_cause, DNA gyrase, Genome, Machine Learning, Ciprofloxacin, Escherichia coli Protein, lcsh:Science, 0303 health sciences, Multidisciplinary, Phylogenetic tree, Escherichia coli Proteins, Intracellular Signaling Peptides and Proteins, Anti-Bacterial Agents, 3. Good health, DNA Gyrase, Mutation (genetic algorithm), medicine.drug, FASTQ format, medicine.drug_class, 030106 microbiology, Biology, Machine learning, Microbiology, DNA sequencing, Article, Inhibitory Concentration 50, 03 medical and health sciences, Minimum inhibitory concentration, Antibiotic resistance, Toxicity Tests, Anti-Bacterial Agent, Drug Resistance, Bacterial, Escherichia coli, medicine, 030304 developmental biology, Whole genome sequencing, Molecular epidemiology, 030306 microbiology, business.industry, lcsh:R, Computational biology and bioinformatics, Toxicity Test, 030104 developmental biology, Intracellular Signaling Peptides and Protein, Genes, Bacterial, Mutation, lcsh:Q, Artificial intelligence, business, computer
Abstract

2.AbstractA possible way to tackle the crisis of antimicrobial resistance development is a strict policy when prescribing antibiotics. Thus, it is important that prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing (NGS), bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR.This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes (ARG). We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of any qnrS gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 92% of the test samples’ MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work goes further than the typical predictions that use machine learning as a black box model concept. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.3.Impact statementWhole genome sequencing has become the standard approach to study molecular epidemiology of bacteria. However, the application of WGS in the clinical microbiology laboratory as part of individual patient diagnostics still requires significant steps forward, in particular with respect to prediction of antibiotic susceptibility based on DNA sequence. Whilst the majority of studies of prediction of susceptibility have used a binary outcome (susceptible/resistant), a quantitative prediction of susceptibility, such as the MIC, will allow for earlier detection of trends in increasing resistance as well as the flexibility to follow potential adjustments in definitions of susceptible (wild type) and resistant (non-wild type) categories (breakpoints/ epidemiological cut-off values).4.Data summaryIn this study, 704 E. coli genomes combined with MIC measurement for ciprofloxacin were analysed (24). Paired-end sequencing was performed on all isolates and the results were stored in FASTQ format. The isolates originated from five countries, Denmark, Italy, USA, UK, and Vietnam. The MIC distribution for these isolates is depicted in Table 1. Out of 704, 266 E. coli genomes had no country metadata available and were used as an independent test set. All data were deposited in the AMR Data Hub (24) which consists of raw sequencing data, ciprofloxacin minimum inhibitory concentrations, and additional metadata such as the origin of the samples.TABLE 1The collected and used data in the analysis grouped by country and MIC values.Publicly available sequencing data was used from projects PRJEB21131, PRJNA266657, PRJNA292901, PRJNA292904, PRJNA292902, PRJDB7087, PRJEB21880, PRJEB21997, PRJEB14086 and PRJEB16326.Download and analysis scripts are available at https://github.com/patbaa/AMR_ciprofloxacin. iTOL phylogenetic tree is available at https://itol.embl.de/tree/14511722611491391569485969.The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

Published
2019
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17. Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks

Author

Aytan-Aktug, D., Clausen, P. T. L. C., Bortolaia, V., Aarestrup, F. M., and Lund, O.

Subjects
0301 basic medicine, Physiology, Computer science, 030106 microbiology, lcsh:QR1-502, Computational biology, Biochemistry, Microbiology, lcsh:Microbiology, Clinical Science and Epidemiology, Mycobacterium tuberculosis, 03 medical and health sciences, Data sequences, Antibiotic resistance, SDG 3 - Good Health and Well-being, Genetics, Feature (machine learning), antimicrobial resistance, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Artificial neural network, biology, neural networks, Multiple species, biology.organism_classification, QR1-502, 3. Good health, Computer Science Applications, machine learning, 030104 developmental biology, Modeling and Simulation, amr, Research Article
Abstract

Machine learning is a proven method to predict AMR; however, the performance of any machine learning model depends on the quality of the input data. Therefore, we evaluated different methods of representing information about mutations as well as mobilizable genes, so that the information can serve as input for a robust model. We combined data from multiple bacterial species in order to develop species-independent machine learning models that can predict resistance profiles for multiple antimicrobials and species with high performance., Machine learning has proven to be a powerful method to predict antimicrobial resistance (AMR) without using prior knowledge for selected bacterial species-antimicrobial combinations. To date, only species-specific machine learning models have been developed, and to the best of our knowledge, the inclusion of information from multiple species has not been attempted. The aim of this study was to determine the feasibility of including information from multiple bacterial species to predict AMR for an individual species, since this may make it easier to train and update resistance predictions for multiple species and may lead to improved predictions. Whole-genome sequence data and susceptibility profiles from 3,528 Mycobacterium tuberculosis, 1,694 Escherichia coli, 658 Salmonella enterica, and 1,236 Staphylococcus aureus isolates were included. We developed machine learning models trained by the features of the PointFinder and ResFinder programs detected to predict binary (susceptible/resistant) AMR profiles. We tested four feature representation methods to determine the most efficient way for introducing features into the models. When training the model only on the Mycobacterium tuberculosis isolates, high prediction performances were obtained for the six AMR profiles included. By adding information on ciprofloxacin from the additional 3,588 isolates, there was no reduction in performance for the other antimicrobials but an increased performance for ciprofloxacin AMR profile prediction for Mycobacterium tuberculosis and Escherichia coli. In conclusion, the species-independent models can predict multi-AMR profiles for multiple species without losing any robustness. IMPORTANCE Machine learning is a proven method to predict AMR; however, the performance of any machine learning model depends on the quality of the input data. Therefore, we evaluated different methods of representing information about mutations as well as mobilizable genes, so that the information can serve as input for a robust model. We combined data from multiple bacterial species in order to develop species-independent machine learning models that can predict resistance profiles for multiple antimicrobials and species with high performance.

Published
2020

18. Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

Author

Pataki, B.Á., Matamoros, S, van der Putten, B.C.L., Remondini, D., Giampieri, E., Aytan-Aktug, D., Hendriksen, R.S., Lund, O., Csabai, I., Schultsz, C. (Constance), Pataki, B.Á., Matamoros, S, van der Putten, B.C.L., Remondini, D., Giampieri, E., Aytan-Aktug, D., Hendriksen, R.S., Lund, O., Csabai, I., and Schultsz, C. (Constance)

Published
2020
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19. Accelerating surveillance and research of antimicrobial resistance - an online repository for sharing of antimicrobial susceptibility data associated with whole-genome sequences

Author

Matamoros, S. (Sébastien), Hendriksen, R.S. (Rene S.), Pataki, B.Á. (Bálint Ármin), Pakseresht, N. (Nima), Rossello, M. (Marc), Silvester, N. (Nicole), Amid, C. (Clara), Aarestrup, F.M. (Frank), Koopmans D.V.M., M.P.G. (Marion), Cochrane, G. (Guy), Csabai, I. (Istvan), Lund, O. (Ole), Schultsz, C. (Constance), The Compare Ml-Amr Group, (), Matamoros, S. (Sébastien), Hendriksen, R.S. (Rene S.), Pataki, B.Á. (Bálint Ármin), Pakseresht, N. (Nima), Rossello, M. (Marc), Silvester, N. (Nicole), Amid, C. (Clara), Aarestrup, F.M. (Frank), Koopmans D.V.M., M.P.G. (Marion), Cochrane, G. (Guy), Csabai, I. (Istvan), Lund, O. (Ole), Schultsz, C. (Constance), and The Compare Ml-Amr Group, ()

Abstract

Antimicrobial resistance (AMR) is an emerging threat to modern medicine. Improved diagnostics and surveillance of resistant bacteria require the development of next-generation analysis tools and collabora

Published
2020
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20. Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

Author

Pataki, B., Matamoros, S., van der Putten, Boas C.L., Remondini, Daniel, Giampieri, Enrico, Aytan-Aktug, Derya, Hendriksen, Rene S., Lund, O., Csabai, István, Schultsz, C., Pataki, B., Matamoros, S., van der Putten, Boas C.L., Remondini, Daniel, Giampieri, Enrico, Aytan-Aktug, Derya, Hendriksen, Rene S., Lund, O., Csabai, István, and Schultsz, C.

Abstract

It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples’ MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.

Published
2020

21. Accelerating surveillance and research of antimicrobial resistance - an online repository for sharing of antimicrobial susceptibility data associated with whole-genome sequences

Author

Matamoros, S, Hendriksen, RS, Pataki, BA, Pakseresht, N, Rossello, M, Silvester, N, Amid, C, Aarestrup, FM, Koopmans, Marion, Cochrane, G, Csabai, I, Lund, O, Schultsz, C, Matamoros, S, Hendriksen, RS, Pataki, BA, Pakseresht, N, Rossello, M, Silvester, N, Amid, C, Aarestrup, FM, Koopmans, Marion, Cochrane, G, Csabai, I, Lund, O, and Schultsz, C

Published
2020

23. Incidence of hypo- and hyper-capnia in a cross-sectional European cohort of ventilated newborn infants

Author

van Kaam, Anton H, De Jaegere, Anne P, Rimensberger, Peter C, Debeer, A., Chemin, A., Norbert, K., Autret, F., Andreou, A., Kroon, A., Minić, A., Schwindt, J., Brouwers, H., van Reempts, P., Hummler, H., van Veenendaal, M., Sarafidis, K., Lopriore, E., Mosca, F., McCormick, K., Schaible, T., Jaarsma, A., Polimeni, V., Plavka, R., Patkai, J., Moriette, G., Valls, A., Soler, I., Clarke, P., Migliori, C., Hentschel, R., Sigalas, J., Ehlen, M., Fremerey, C., Roujou-Gris, M., Stamatin, M., Mok, Q., Ata, S., Günther, M., Kühr, J., Seitz, U., Vermeulen, M., Knol, R., Le Bouedec, S., Szekessy, D., Wauer, R., Petropoulou, C., Moreno Hernando, J., Jonsson, B., Mulder, T., Sweet, D., Herting, E., Goepel, W., Dimitriou, G., Stuchlíková, H., Baltogianni, M., Santillo, V., Ferrero, F., Arnault, I., Dort, J., Blanc, T., Rocha, G., Guimarães, H., Virella, D., Costa, A., Pedro Frutuoso, S., Biolek, J., Stoicescu, S., Schroth, M., Cirstoveanu, C., de Boode, W., Medbo, S., Müller-Hansen, I., Poets, C., Riedel, T., Palmer, K., Martano, C., Stucin Gantar, I., Biban, P., Chatfield, S., Ghesquiere, J., Theret, B., Samperiz, S., Berger, T., Rigo, V., Balato, A., Gresa Munoz, M., Nunes, A., Molendijk, H., Beuger, S., Puzas, A., Hiedl, S., Genzel-Boroviczeny, O., Anhalt, D., Möller, J., Ingemansson, F., Halbertsma, F., de Cesaris, V., Saarela, T., Karagianni, P., Tsakalidis, C., Tølløfsrud, P., Bougatef, A., Sindelar, R., Wisborg, K., Brink Henriksen, T., Flumini, C., Carnielli, V., Giannuzzo, S., Dussart, A., Brault, D., Samy, M., van Wien, A., Cunha, M., Paulino, E., Schneider, H., Sandvoss, A., Dahlem, P., Koester, B., Olhanger, E., Wentzell, R., Ramos, C., Augusta Areias, M., Verber, I., Presta, G., Magaldi, R., Agostino, R., Lund, O., Ulriksen, J., Steder, U., Faas, D., Jensen, R., Baroutis, G., Gouder de Beauregard, V., Zaharie, G., Eng-Schwartz, A., Heldmann, M., Cezanne, T., Pereira, A., Nelle, M., Uxa, F., Norman, M., Siegel, J., Welsch, M., Schiffmann, H., Haftel, L., Wild, F., Bühr, P., Simma, B., Thirumurugan, A., Mortensen, S., Ciccotti, R., Carli, G., Milligan, D., Gerleve, H., Kumararatne, B., Hakansson, S., OʼDonovan, D., Reiterer, F., Rimensberger, P., Nietsch, L., Nakstad, B., Gancia, P., Swanstrom, S., Maton, P., Cavatorta, E., Tvarijonoviciene, R., Hogan, M., Zinn, P., Freff, M., Reigstad, H., Olariu, G., Gonçalves, G., Escumalha, M., Ornelas, H., Serrano, A., Anderssen, S., Garcia, P., Mendes Da Graça, A., Bender, C., Wald, M., Bohn, M., Schnelke, A., Trips, T., Ladekjaer, J., Thompson, F., Lindberg, E., Frigerio, M., Pederzini, F., De Nisi, G., Saur, G., Losa, M., Toma, A., Matu, E., Eckhardt, S., Bellettato, M., Fahnenstich, H., Hetzel, P., Bland, J., Øglænd, B., Lehtonen, L., Eichler, T., Roth, M., Meberg, A., Kuehn, T., and Emeis, M.

Published
2013
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29. The Immune Epitope Database and Analysis Resource

Author

Sette, A., primary, Bui, H. H., additional, Sidney, J., additional, Bourne, P., additional, Buus, S., additional, Fleri, W., additional, Kubo, R., additional, Lund, O., additional, Nemazee, D., additional, Ponomarenko, J. V., additional, Sathiamurthy, M., additional, Stewart, S., additional, Way, S., additional, Wilson, S. S., additional, and Peters, B., additional

Published
2006
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32. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage

Author

Hendriksen, R.S., Munk, P., Njage, P., van Bunnik, B., McNally, L., Lukjancenko, O., Röder, T., Nieuwenhuijse, D., Pedersen, S.K., Kjeldgaard, J., Kaas, R.S., Clausen, P.T.L.C., Vogt, J.K., Leekitcharoenphon, P., van de Schans, M.G.M., Zuidema, T., de Roda Husman, A.M., Rasmussen, S., Petersen, B., Bego, A., Rees, C., Cassar, S., Coventry, K., Collignon, P., Allerberger, F., Rahube, T.O., Oliveira, G., Ivanov, I., Vuthy, Y., Sopheak, T., Yost, C.K., Ke, C., Zheng, H., Baisheng, L., Jiao, X., Donado-Godoy, P., Coulibaly, K.J., Jergović, M., Hrenovic, J., Karpíšková, R., Villacis, J.E., Legesse, M., Eguale, T., Heikinheimo, A., Malania, L., Nitsche, A., Brinkmann, A., Saba, C.K.S., Kocsis, B., Solymosi, N., Thorsteinsdottir, T.R., Hatha, A.M., Alebouyeh, M., Morris, D., Cormican, M., O’Connor, L., Moran-Gilad, J., Alba, P., Battisti, A., Shakenova, Z., Kiiyukia, C., Ng’eno, E., Raka, L., Avsejenko, J., Bērziņš, A., Bartkevics, V., Penny, C., Rajandas, H., Parimannan, S., Haber, M.V., Pal, P., Jeunen, G.-J., Gemmell, N., Fashae, K., Holmstad, R., Hasan, R., Shakoor, S., Rojas, M.L.Z., Wasyl, D., Bosevska, G., Kochubovski, M., Radu, C., Gassama, A., Radosavljevic, V., Wuertz, S., Zuniga-Montanez, R., Tay, M.Y.F., Gavačová, D., Pastuchova, K., Truska, P., Trkov, M., Esterhuyse, K., Keddy, K., Cerdà-Cuéllar, M., Pathirage, S., Norrgren, L., Örn, S., Larsson, D.G.J., Heijden, T.V., Kumburu, H.H., Sanneh, B., Bidjada, P., Njanpop-Lafourcade, B.-M., Nikiema-Pessinaba, S.C., Levent, B., Meschke, J.S., Beck, N.K., Van, C.D., Phuc, N.D., Tran, D.M.N., Kwenda, G., Tabo, D.-A., Wester, A.L., Cuadros-Orellana, S., Amid, C., Cochrane, G., Sicheritz-Ponten, T., Schmitt, H., Alvarez, J.R.M., Aidara-Kane, A., Pamp, S.J., Lund, O., Hald, T., Woolhouse, M., Koopmans, M.P., Vigre, H., Petersen, T.N., Aarestrup, F.M.

Subjects
Global surveillance, antimicrobial resistance, AMR, wastewater, sewage, metagenomics, resistome, machine learning, prediction
Abstract

Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use metagenomics analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of the AMR variation that we observe. We find no evidence for cross- selection between antimicrobial classes, or for effect of air travel between sites. However, AMR gene abundance strongly correlates with socioeconomic, health and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region, and that improving sanitation and health could potentially limit the global burden of AMR. We propose metagenomic analysis of sewage as an ethically acceptable and economically feasible approach for continuous global surveillance and prediction of AMR.

Published
2019

33. The COMPARE Data Hubs

Author

Amid, C. (Clara), Pakseresht, N. (Nima), Silvester, N. (Nicole), Jayathilaka, S. (Suran), Lund, O. (Ole), Dynovski, L.D. (Lukasz D.), Pataki, B.Á. (Bálint Á), Visontai, D. (Dávid), Xavier, B.B. (Basil Britto), Alako, B.T.F. (Blaise), Belka, A. (Ariane), Cisneros, J.L.B. (Jose L B), Cotten, M. (Matthew), Haringhuizen, G.B. (George B.), Harrison, P.W. (Peter W.), Höper, D. (Dirk), Holt, S. (Sam), Hundahl, C. (Camilla), Hussein, A. (Abdulrahman), Kaas, R.S. (Rolf S.), Liu, X. (Xin), Leinonen, R. (Rasko), Malhotra-Kumar, S. (Surbhi), Nieuwenhuijse, D.F. (David F.), Rahman, N. (Nadim), Dos S Ribeiro, C. (Carolina), Skiby, J.E. (Jeffrey E.), Schmitz, D. (Dennis), Stéger, J. (József), Szalai-Gindl, J.M. (János M), Thomsen, M.C.F. (Martin C F), Caccio, S. (Simone), Csabai, I. (István), Kroneman, A. (Annelies), Koopmans D.V.M., M.P.G. (Marion), Aarestrup, F.M. (Frank), Cochrane, G. (Guy), Amid, C. (Clara), Pakseresht, N. (Nima), Silvester, N. (Nicole), Jayathilaka, S. (Suran), Lund, O. (Ole), Dynovski, L.D. (Lukasz D.), Pataki, B.Á. (Bálint Á), Visontai, D. (Dávid), Xavier, B.B. (Basil Britto), Alako, B.T.F. (Blaise), Belka, A. (Ariane), Cisneros, J.L.B. (Jose L B), Cotten, M. (Matthew), Haringhuizen, G.B. (George B.), Harrison, P.W. (Peter W.), Höper, D. (Dirk), Holt, S. (Sam), Hundahl, C. (Camilla), Hussein, A. (Abdulrahman), Kaas, R.S. (Rolf S.), Liu, X. (Xin), Leinonen, R. (Rasko), Malhotra-Kumar, S. (Surbhi), Nieuwenhuijse, D.F. (David F.), Rahman, N. (Nadim), Dos S Ribeiro, C. (Carolina), Skiby, J.E. (Jeffrey E.), Schmitz, D. (Dennis), Stéger, J. (József), Szalai-Gindl, J.M. (János M), Thomsen, M.C.F. (Martin C F), Caccio, S. (Simone), Csabai, I. (István), Kroneman, A. (Annelies), Koopmans D.V.M., M.P.G. (Marion), Aarestrup, F.M. (Frank), and Cochrane, G. (Guy)

Abstract

Data sharing enables research communities to exchange findings and build upon the knowledge that arises from their discoveries. Areas of public and animal health as well as food safety would benefit from rapid data sharing when it comes to emergencies. However, ethical, regulatory and institutional challenges, as well as lack of suitable platforms which provide an infrastructure for data sharing in structured formats, often lead to data not being shared or at most shared in form of supplementary materials in journal publications. Here, we describe an informatics platform that includes workflows for structured data storage, managing and pre-publication sharing of pathogen sequencing data and its analysis interpretations with relevant stakeholders.

Published
2019
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34. Specific PCR-based detection of Phomopsis vexans the cause of leaf blight and fruit rot pathogen of Solanum melongena L.

Author

Udayashankar, A. C., Chandra Nayaka, S., Archana, B., Lakshmeesha, T. R., Niranjana, S. R., Lund, O. S., Prakash, H. S., Udayashankar, A. C., Chandra Nayaka, S., Archana, B., Lakshmeesha, T. R., Niranjana, S. R., Lund, O. S., and Prakash, H. S.

Abstract

Leaf blight and fruit rot disease caused by Phomopsis vexans is a devastating disease of brinjal. The detection of P. vexans in plant parts and seeds of brinjal can be complicated, mainly when the inoculum is present at low levels and/or overgrown by fast-growing saprophytic fungi or other seed-borne fungi. A PCR-based diagnostic method was developed with specific primers designed based on sequence data of a region consisting of the 5·8S RNA gene and internal transcribed spacers, ITS 1 and ITS 2 of nuclear ribosomal RNA gene (rDNA) repeats of P. vexans. The efficiency and specificity of primer pairs PvexF/PvexR designed were established by PCR analysis of DNA from P. vexans strains isolated from India and fungal isolates of other genera. A single amplification product of 323-bp was detected from DNA of P. vexans isolates. No cross-reaction was observed with any of the other isolates tested. The specific primers designed and employed in PCR detected P. vexans up to 10 pg from DNA isolated from pure culture. This is the first report on the development of species-specific PCR assay for identification and detection of P. vexans. Thus, PCR-based assay developed is very specific, rapid, confirmatory and sensitive tool for the detection of pathogen P. vexans at early stages. Significance and Impact of the Study: Phomopsis vexans is an important seed-borne pathogenic fungus responsible for leaf blight and fruit rot in brinjal. Current detection methods, based on culture and morphological identification is time consuming, laborious and are not always reliable. A PCR-based diagnostic method was developed with species-specific primers designed based on sequence data of a region consisting of the 5·8S RNA gene and internal transcribed spacers, ITS 1 and ITS 2 of nuclear ribosomal RNA gene (rDNA) repeats of P. vexans.

Published
2019

36. Keratoprothetik

Author

Lund, O.-E., Heberer, G., editor, Pichlmayr, R., editor, Mackensen, G., editor, Neubauer, H., editor, Bigar, F., editor, Draeger, J., editor, Hanselmayer, H., editor, Henschel, W. F., editor, Hoffmann, F., editor, Hofmann, H., editor, Kirchner, M., editor, Kommerell, G., editor, Lund, O.-E., editor, Matthes, K., editor, Naumann, G. O. H., editor, Rüssmann, W., editor, Sautter, H., editor, Sundmacher, R., editor, Ullerich, K., editor, and Vossen, J., editor

Published
1988
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38. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes

Author

Nielsen, H.B., Almeida, M., Sierakowska Juncker, A., Rasmussen, S., Li, J., Sunagawa, S., Plichta, D.R., Gautier, L., Pedersen, A.G., Le Chatelier, E., Pelletier, E., Bonde, I., Nielsen, T., Manichanh, C., Arumugam, M., Batto, J.M., Quintanilha dos Santos, M.B., Blom, N., Borruel, N., Burgdorf, K.S., Boumezbeur, F., Casellas, F., Doré, J., Dworzynski, P., Guarner, F., Hansen, T., Hildebrand, F., Kaas, R.S., Kennedy, S., Kristiansen, K., Kultima, J.R., Leonard, P., Levenez, F., Lund, O., Moumen, B., Le Paslier, D., Pons, N., Pedersen, O., Prifti, E., Qin, J., Raes, J., Sørensen, S., Tap, J., Tims, S., Ussery, D.W., Yamada, T., Jamet, A., Mérieux, A., Cultrone, A., Torrejon, A., Quinquis, B., Brechot, C., Delorme, C., M'Rini, C., de Vos, W.M., Maguin, E., Varela, E., Guedon, E., Gwen, F., Haimet, F., Artiguenave, F., Vandemeulebrouck, G., Denariaz, G., Khaci, G., Blottière, H., Knol, J., Weissenbach, J., van Hylckama Vlieg, J.E., Torben, J., Parkhil, J., Turner, K., van de Guchte, M., Antolin, M., Rescigno, M., Kleerebezem, M., Derrien, M., Galleron, N., Sanchez, N., Grarup, N., Veiga, P., Oozeer, R., Dervyn, R., Layec, S., Bruls, T., Winogradski, Y., Zoetendal, E.G., Renault, D., Sicheritz-Ponten, Bork, P., Wang, J., Brunak, S., Ehrlich, S.D., Center for Biological Sequence Analysis, Technical University of Denmark [Lyngby] (DTU), Novo Nordisk Foundation Center for Biosustainability, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of Computer Science [Baltimore], Johns Hopkins University (JHU), BGI Hong Kong Researche Institute, BGI Shenzhen, School of Bioscience and Biotechnology, Southern University of Science and Technology [Shenzhen] (SUSTech), European Molecular Biology Laboratory, US 1367 MetaGénoPolis, Institut National de la Recherche Agronomique (INRA)-Département Microbiologie et Chaîne Alimentaire (MICA), Institut National de la Recherche Agronomique (INRA)-MetaGénoPolis (MGP), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Université d'Évry-Val-d'Essonne (UEVE), Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Digestive System Research Unit, Vall d'Hebron University Hospital [Barcelona], Faculty of Health Sciences, University of Southern Denmark (SDU), Department of Structural Biology, Flanders Institute for Biotechnology, Department of Bioscience Engineering, Vrije Universiteit [Brussels] (VUB), 8National Food Institute - Division for Epidemiology and Microbial Genomics, Department of Biology [Copenhagen], Faculty of Science [Copenhagen], Hagedorn Research Institute, Faculty of Health, Aarhus University [Aarhus], BGI Hong Kong research Institute, Rega Institute - Department of Microbiology and Immunology, Université Catholique de Louvain (UCL), VIB Center for the Biology of Disease, Section of Microbiology [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Faculty of Science [Copenhagen], Laboratory of Microbiology, Wageningen University and Research Centre [Wageningen] (WUR), Department of Biological Information, Tokyo Institute of Technology [Tokyo] (TITECH), Max-Delbrück Center for Molecular Medicine, Princess Al Jawhara Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Centre for Host-Microbiome Interactions, Dental Institute Central Office, Guy’s Hospital, King‘s College London, Département Microbiologie et Chaîne Alimentaire (MICA), Institut National de la Recherche Agronomique (INRA), European Community's Seventh Framework Programme [FP7-HEALTH-F4-2007-201052, FP7-HEALTH-2010-261376], OpenGPU FUI collaborative research projects, DGCIS, Instituto de Salud Carlos III (Spain), Ministere de la Recherche et de l'Education Nationale (France), [ANR-11-DPBS-0001], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Beijing Genomics Institute [Shenzhen] (BGI), Southern University of Science and Technology (SUSTech), MetaGenoPolis, Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Vrije Universiteit Brussel (VUB), Université Catholique de Louvain = Catholic University of Louvain (UCL), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Faculty of Science [Copenhagen], Wageningen University and Research [Wageningen] (WUR), Max Delbrück Center for Molecular Medicine [Berlin] (MDC), Helmholtz-Gemeinschaft = Helmholtz Association, European Project: 201052,EC:FP7:HEALTH,FP7-HEALTH-2007-A,METAHIT(2008), Department of Systems Biology, Center for Biological Sequence Analysis, Ctr Biol Sequence Anal, National University of Singapore (NUS), European Molecular Biology Laboratory [Heidelberg] (EMBL), Department of Mathematics and Computer Science [Odense] (IMADA), Génomique métabolique (UMR 8030), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Vall d’Hebron Research Institute (VHIR), Faculty of Health and Medical Sciences, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen = Københavns Universitet (KU), INRA US1367 MetaGenoPolis, European Molecular Biology Laboratory [Grenoble] (EMBL), Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Institute of cardiometabolism and nutrition (ICAN), Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP], Center for Biological Sequence Analysis [Lyngby], Chinese Academy of Agricultural Mechanization Sciences (CCCME), 1Génétique Microbienne, INRA, Domaine de Vilvert, 78352 Jouy en Josas Cedex, and Department of Bio-engineering Sciences

Subjects
Cellular immunity, polypeptide, [SDV]Life Sciences [q-bio], SHORT READ ALIGNMENT SEQUENCES SYSTEMS ALGORITHMS MICROBIOTA PROTEIN LIFE SETS TREE TOOL, complex metagenomic sample, Applied Microbiology and Biotechnology, Genome, Microbiologie, Databases, Genetic, genetic element, Cluster Analysis, sets, short read alignment, ComputingMilieux_MISCELLANEOUS, Genetics, 0303 health sciences, tool, metagenomic, tree, Lactococcus lactis, IL-12, Molecular Medicine, Biotechnology, life, Microbial Genomes, antigen specific immune response, Biomedical Engineering, Bioengineering, Computational biology, [SDV.BID]Life Sciences [q-bio]/Biodiversity, cellular immunity, Biology, algorithms, Microbiology, 03 medical and health sciences, Genetic variation, microbiota, Microbiome, Gene, genome, 030304 developmental biology, adjuvant activity, VLAG, 030306 microbiology, Metagenomics, WIAS, Microbial genetics, sequences, systems, protein
Abstract

Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.

Published
2014

39. Abstracts from Hydrocephalus 2016.

Author

Adam, A, Robison, J, Lu, J, Jose, R, Badran, N, Vivas-Buitrago, T, Rigamonti, D, Sattar, A, Omoush, O, Hammad, M, Dawood, M, Maghaslah, M, Belcher, T, Carson, K, Hoffberger, J, Jusué Torres, I, Foley, S, Yasar, S, Thai, Q A, Wemmer, J, Klinge, P, Al-Mutawa, L, Al-Ghamdi, H, Carson, K A, Asgari, M, de Zélicourt, D, Kurtcuoglu, V, Garnotel, S, Salmon, S, Balédent, O, Lokossou, A, Page, G, Balardy, L, Czosnyka, Z, Payoux, P, Schmidt, E A, Zitoun, M, Sevestre, M A, Alperin, N, Baudracco, I, Craven, C, Matloob, S, Thompson, S, Haylock Vize, P, Thorne, L, Watkins, L D, Toma, A K, Bechter, Karl, Pong, A C, Jugé, L, Bilston, L E, Cheng, S, Bradley, W, Hakim, F, Ramón, J F, Cárdenas, M F, Davidson, J S, García, C, González, D, Bermúdez, S, Useche, N, Mejía, J A, Mayorga, P, Cruz, F, Martinez, C, Matiz, M C, Vallejo, M, Ghotme, K, Soto, H A, Riveros, D, Buitrago, A, Mora, M, Murcia, L, Bermudez, S, Cohen, D, Dasgupta, D, Curtis, C, Domínguez, L, Remolina, A J, Grijalba, M A, Whitehouse, K J, Edwards, R J, Eleftheriou, A, Lundin, F, Fountas, K N, Kapsalaki, E Z, Smisson, H F, Robinson, J S, Fritsch, M J, Arouk, W, Garzon, M, Kang, M, Sandhu, K, Baghawatti, D, Aquilina, K, James, G, Thompson, D, Gehlen, M, Schmid Daners, M, Eklund, A, Malm, J, Gomez, D, Guerra, M, Jara, M, Flores, M, Vío, K, Moreno, I, Rodríguez, S, Ortega, E, Rodríguez, E M, McAllister, J P, Guerra, M M, Morales, D M, Sival, D, Jimenez, A, Limbrick, D D, Ishikawa, M, Yamada, S, Yamamoto, K, Junkkari, A, Häyrinen, A, Rauramaa, T, Sintonen, H, Nerg, O, Koivisto, A M, Roine, R P, Viinamäki, H, Soininen, H, Luikku, A, Jääskeläinen, J E, Leinonen, V, Kehler, U, Lilja-Lund, O, Kockum, K, Larsson, Elna-Marie, Riklund, K, Söderström, L, Hellström, P, Laurell, K, Kojoukhova, M, Sutela, A, Vanninen, R, Vanha, K I, Timonen, M, Rummukainen, J, Korhonen, V, Helisalmi, S, Solje, E, Remes, A M, Huovinen, J, Paananen, J, Hiltunen, M, Kurki, M, Martin, B, Loth, F, Luciano, M, Luikku, A J, Hall, A, Herukka, S K, Mattila, J, Lötjönen, J, Alafuzoff, Irina, Jurjević, I, Miyajima, M, Nakajima, M, Murai, H, Shin, T, Kawaguchi, D, Akiba, C, Ogino, I, Karagiozov, K, Arai, H, Reis, R C, Teixeira, M J, Valêncio, C G, da Vigua, D, Almeida-Lopes, L, Mancini, M W, Pinto, F C G, Maykot, R H, Calia, G, Tornai, J, Silvestre, S S S, Mendes, G, Sousa, V, Bezerra, B, Dutra, P, Modesto, P, Oliveira, M F, Petitto, C E, Pulhorn, H, Chandran, A, McMahon, C, Rao, A S, Jumaly, M, Solomon, D, Moghekar, A, Relkin, N, Hamilton, M, Katzen, H, Williams, M, Bach, T, Zuspan, S, Holubkov, R, Rigamonti, A, Clemens, G, Sharkey, P, Sanyal, A, Sankey, E, Rigamonti, K, Naqvi, S, Hung, A, Schmidt, E, Ory-Magne, F, Gantet, P, Guenego, A, Januel, A C, Tall, P, Fabre, N, Mahieu, L, Cognard, C, Gray, L, Buttner-Ennever, J A, Takagi, K, Onouchi, K, Thompson, S D, Thorne, L D, Tully, H M, Wenger, T L, Kukull, W A, Doherty, D, Dobyns, W B, Moran, D, Vakili, S, Patel, M A, Elder, B, Goodwin, C R, Crawford, J A, Pletnikov, M V, Xu, J, Blitz, A, Herzka, D A, Guerrero-Cazares, H, Quiñones-Hinojosa, A, Mori, S, Saavedra, P, Treviño, H, Maitani, K, Ziai, W C, Eslami, V, Nekoovaght-Tak, S, Dlugash, R, Yenokyan, G, McBee, N, Hanley, D F, Adam, A, Robison, J, Lu, J, Jose, R, Badran, N, Vivas-Buitrago, T, Rigamonti, D, Sattar, A, Omoush, O, Hammad, M, Dawood, M, Maghaslah, M, Belcher, T, Carson, K, Hoffberger, J, Jusué Torres, I, Foley, S, Yasar, S, Thai, Q A, Wemmer, J, Klinge, P, Al-Mutawa, L, Al-Ghamdi, H, Carson, K A, Asgari, M, de Zélicourt, D, Kurtcuoglu, V, Garnotel, S, Salmon, S, Balédent, O, Lokossou, A, Page, G, Balardy, L, Czosnyka, Z, Payoux, P, Schmidt, E A, Zitoun, M, Sevestre, M A, Alperin, N, Baudracco, I, Craven, C, Matloob, S, Thompson, S, Haylock Vize, P, Thorne, L, Watkins, L D, Toma, A K, Bechter, Karl, Pong, A C, Jugé, L, Bilston, L E, Cheng, S, Bradley, W, Hakim, F, Ramón, J F, Cárdenas, M F, Davidson, J S, García, C, González, D, Bermúdez, S, Useche, N, Mejía, J A, Mayorga, P, Cruz, F, Martinez, C, Matiz, M C, Vallejo, M, Ghotme, K, Soto, H A, Riveros, D, Buitrago, A, Mora, M, Murcia, L, Bermudez, S, Cohen, D, Dasgupta, D, Curtis, C, Domínguez, L, Remolina, A J, Grijalba, M A, Whitehouse, K J, Edwards, R J, Eleftheriou, A, Lundin, F, Fountas, K N, Kapsalaki, E Z, Smisson, H F, Robinson, J S, Fritsch, M J, Arouk, W, Garzon, M, Kang, M, Sandhu, K, Baghawatti, D, Aquilina, K, James, G, Thompson, D, Gehlen, M, Schmid Daners, M, Eklund, A, Malm, J, Gomez, D, Guerra, M, Jara, M, Flores, M, Vío, K, Moreno, I, Rodríguez, S, Ortega, E, Rodríguez, E M, McAllister, J P, Guerra, M M, Morales, D M, Sival, D, Jimenez, A, Limbrick, D D, Ishikawa, M, Yamada, S, Yamamoto, K, Junkkari, A, Häyrinen, A, Rauramaa, T, Sintonen, H, Nerg, O, Koivisto, A M, Roine, R P, Viinamäki, H, Soininen, H, Luikku, A, Jääskeläinen, J E, Leinonen, V, Kehler, U, Lilja-Lund, O, Kockum, K, Larsson, Elna-Marie, Riklund, K, Söderström, L, Hellström, P, Laurell, K, Kojoukhova, M, Sutela, A, Vanninen, R, Vanha, K I, Timonen, M, Rummukainen, J, Korhonen, V, Helisalmi, S, Solje, E, Remes, A M, Huovinen, J, Paananen, J, Hiltunen, M, Kurki, M, Martin, B, Loth, F, Luciano, M, Luikku, A J, Hall, A, Herukka, S K, Mattila, J, Lötjönen, J, Alafuzoff, Irina, Jurjević, I, Miyajima, M, Nakajima, M, Murai, H, Shin, T, Kawaguchi, D, Akiba, C, Ogino, I, Karagiozov, K, Arai, H, Reis, R C, Teixeira, M J, Valêncio, C G, da Vigua, D, Almeida-Lopes, L, Mancini, M W, Pinto, F C G, Maykot, R H, Calia, G, Tornai, J, Silvestre, S S S, Mendes, G, Sousa, V, Bezerra, B, Dutra, P, Modesto, P, Oliveira, M F, Petitto, C E, Pulhorn, H, Chandran, A, McMahon, C, Rao, A S, Jumaly, M, Solomon, D, Moghekar, A, Relkin, N, Hamilton, M, Katzen, H, Williams, M, Bach, T, Zuspan, S, Holubkov, R, Rigamonti, A, Clemens, G, Sharkey, P, Sanyal, A, Sankey, E, Rigamonti, K, Naqvi, S, Hung, A, Schmidt, E, Ory-Magne, F, Gantet, P, Guenego, A, Januel, A C, Tall, P, Fabre, N, Mahieu, L, Cognard, C, Gray, L, Buttner-Ennever, J A, Takagi, K, Onouchi, K, Thompson, S D, Thorne, L D, Tully, H M, Wenger, T L, Kukull, W A, Doherty, D, Dobyns, W B, Moran, D, Vakili, S, Patel, M A, Elder, B, Goodwin, C R, Crawford, J A, Pletnikov, M V, Xu, J, Blitz, A, Herzka, D A, Guerrero-Cazares, H, Quiñones-Hinojosa, A, Mori, S, Saavedra, P, Treviño, H, Maitani, K, Ziai, W C, Eslami, V, Nekoovaght-Tak, S, Dlugash, R, Yenokyan, G, McBee, N, and Hanley, D F

Published
2017
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40. Evaluating next-generation sequencing for direct clinical diagnostics in diarrhoeal disease

Author

Joensen, K G, Engsbro, A L Ø, Lukjancenko, O, Kaas, R S, Lund, O, Westh, H, Aarestrup, F M, Joensen, K G, Engsbro, A L Ø, Lukjancenko, O, Kaas, R S, Lund, O, Westh, H, and Aarestrup, F M

Abstract

The accurate microbiological diagnosis of diarrhoea involves numerous laboratory tests and, often, the pathogen is not identified in time to guide clinical management. With next-generation sequencing (NGS) becoming cheaper, it has huge potential in routine diagnostics. The aim of this study was to evaluate the potential of NGS-based diagnostics through direct sequencing of faecal samples. Fifty-eight clinical faecal samples were obtained from patients with diarrhoea as part of the routine diagnostics at Hvidovre University Hospital, Denmark. Ten samples from healthy individuals were also included. DNA was extracted from faecal samples and sequenced on the Illumina MiSeq system. Species distribution was determined with MGmapper and NGS-based diagnostic prediction was performed based on the relative abundance of pathogenic bacteria and Giardia and detection of pathogen-specific virulence genes. NGS-based diagnostic results were compared to conventional findings for 55 of the diarrhoeal samples; 38 conventionally positive for bacterial pathogens, two positive for Giardia, four positive for virus and 11 conventionally negative. The NGS-based approach enabled detection of the same bacterial pathogens as the classical approach in 34 of the 38 conventionally positive bacterial samples and predicted the responsible pathogens in five of the 11 conventionally negative samples. Overall, the NGS-based approach enabled pathogen detection comparable to conventional diagnostics and the approach has potential to be extended for the detection of all pathogens. At present, however, this approach is too expensive and time-consuming for routine diagnostics.

Published
2017

44. Hospital epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) in a tertiary care hospital in Moshi Tanzania as determined by whole genome sequencing

Author

Kumburu, H. H., Sonda, T., Leetcharoenphon, P., van Zwetselaar, M., Lukjancenko, O., Alifrangis, M., Lund, O., Mmbaga, B. T., Kibiki, G., Aarestrup, F. M., Kumburu, H. H., Sonda, T., Leetcharoenphon, P., van Zwetselaar, M., Lukjancenko, O., Alifrangis, M., Lund, O., Mmbaga, B. T., Kibiki, G., and Aarestrup, F. M.

Published
2017

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