IS element IS16 as a molecular screening tool to identify hospital-associated strains of Enterococcus faecium

<p>Abstract</p> <p>Background</p> <p>Hospital strains of <it>Enterococcus faecium </it>could be characterized and typed by various molecular methods (MLST, AFLP, MLVA) and allocated to a distinct clonal complex known as MLST CC17. However, these techniques are laborious, time-consuming and cost-intensive. Our aim was to identify hospital <it>E. faecium </it>strains and differentiate them from colonizing and animal variants by a simple, inexpensive and reliable PCR-based screening assay. We describe here performance and predictive value of a single PCR detecting the insertion element, IS<it>16</it>, to identify hospital <it>E. faecium </it>isolates within a collection of 260 strains of hospital, animal and human commensal origins.</p> <p>Methods</p> <p>Specific primers were selected amplifying a 547-bp fragment of IS<it>16</it>. Presence of IS<it>16 </it>was determined by PCR screenings among the 260 <it>E. faecium </it>isolates. Distribution of IS<it>16 </it>was compared with a prevalence of commonly used markers for hospital strains, <it>esp </it>and <it>hyl</it>_<it>Efm</it>. All isolates were typed by MLST and partly by PFGE. Location of IS<it>16 </it>was analysed by Southern hybridization of plasmid and chromosomal DNA.</p> <p>Results</p> <p>IS<it>16 </it>was exclusively distributed only among 155 invasive strains belonging to the clonal complex of hospital-associated strains ("CC17"; 28 MLST types) and various vancomycin resistance genotypes (<it>van</it>A/B/negative). The five invasive IS<it>16</it>-negative strains did not belong to the clonal complex of hospital-associated strains (CC17). IS<it>16 </it>was absent in all but three isolates from 100 livestock, food-associated and human commensal strains ("non-CC17"; 64 MLST types). The three IS<it>16</it>-positive human commensal isolates revealed MLST types belonging to the clonal complex of hospital-associated strains (CC17). The values predicting a hospital-associated strain ("CC17") deduced from presence and absence of IS<it>16 </it>was 100% and thus superior to screening for the presence of <it>esp </it>(66%) and/or <it>hyl</it>_{<it>Efm </it>}(46%). Southern hybridizations revealed chromosomal as well as plasmid localization of IS<it>16</it>.</p> <p>Conclusions</p> <p>This simple screening assay for insertion element IS<it>16 </it>is capable of differentiating hospital-associated from human commensal, livestock- and food-associated <it>E. faecium </it>strains and thus allows predicting the epidemic strengths or supposed pathogenic potential of a given <it>E. faecium </it>isolate identified within the nosocomial setting.</p

IS element IS16 as a molecular screening tool to identify hospital-associated strains of Enterococcus faecium

<p>Abstract</p> <p>Background</p> <p>Hospital strains of <it>Enterococcus faecium </it>could be characterized and typed by various molecular methods (MLST, AFLP, MLVA) and allocated to a distinct clonal complex known as MLST CC17. However, these techniques are laborious, time-consuming and cost-intensive. Our aim was to identify hospital <it>E. faecium </it>strains and differentiate them from colonizing and animal variants by a simple, inexpensive and reliable PCR-based screening assay. We describe here performance and predictive value of a single PCR detecting the insertion element, IS<it>16</it>, to identify hospital <it>E. faecium </it>isolates within a collection of 260 strains of hospital, animal and human commensal origins.</p> <p>Methods</p> <p>Specific primers were selected amplifying a 547-bp fragment of IS<it>16</it>. Presence of IS<it>16 </it>was determined by PCR screenings among the 260 <it>E. faecium </it>isolates. Distribution of IS<it>16 </it>was compared with a prevalence of commonly used markers for hospital strains, <it>esp </it>and <it>hyl</it>_<it>Efm</it>. All isolates were typed by MLST and partly by PFGE. Location of IS<it>16 </it>was analysed by Southern hybridization of plasmid and chromosomal DNA.</p> <p>Results</p> <p>IS<it>16 </it>was exclusively distributed only among 155 invasive strains belonging to the clonal complex of hospital-associated strains ("CC17"; 28 MLST types) and various vancomycin resistance genotypes (<it>van</it>A/B/negative). The five invasive IS<it>16</it>-negative strains did not belong to the clonal complex of hospital-associated strains (CC17). IS<it>16 </it>was absent in all but three isolates from 100 livestock, food-associated and human commensal strains ("non-CC17"; 64 MLST types). The three IS<it>16</it>-positive human commensal isolates revealed MLST types belonging to the clonal complex of hospital-associated strains (CC17). The values predicting a hospital-associated strain ("CC17") deduced from presence and absence of IS<it>16 </it>was 100% and thus superior to screening for the presence of <it>esp </it>(66%) and/or <it>hyl</it>_{<it>Efm </it>}(46%). Southern hybridizations revealed chromosomal as well as plasmid localization of IS<it>16</it>.</p> <p>Conclusions</p> <p>This simple screening assay for insertion element IS<it>16 </it>is capable of differentiating hospital-associated from human commensal, livestock- and food-associated <it>E. faecium </it>strains and thus allows predicting the epidemic strengths or supposed pathogenic potential of a given <it>E. faecium </it>isolate identified within the nosocomial setting.</p

Spectral modeling of type II supernovae - II. A machine-learning approach to quantitative spectroscopic analysis

There are now hundreds of publicly available supernova spectral time series. Radiative transfer modeling of this data provides insight into the physical properties of these explosions, such as the composition, the density structure, and the intrinsic luminosity, which is invaluable for understanding the supernova progenitors, the explosion mechanism, and for constraining the supernova distance. However, a detailed parameter study of the available data has been out of reach due to the high dimensionality of the problem coupled with the still significant computational expense. We tackle this issue through the use of machine-learning emulators, which are algorithms for high-dimensional interpolation. These use a pre-calculated training dataset to mimic the output of a complex code but with run times that are orders of magnitude shorter. We present the application of such an emulator to synthetic type II supernova spectra generated with the TARDIS radiative transfer code. The results show that with a relatively small training set of 780 spectra we can generate emulated spectra with interpolation uncertainties of less than one percent. We demonstrate the utility of this method by automatic spectral fitting of two well-known type IIP supernovae; as an exemplary application, we determine the supernova distances from the spectral fits using the tailored-expanding-photosphere method. We compare our results to previous studies and find good agreement. This suggests that emulation of TARDIS spectra can likely be used to perform automatic and detailed analysis of many transient classes putting the analysis of large data repositories within reach

Spectral modeling of type II supernovae II. A machine learning approach to quantitative spectroscopic analysis

There are now hundreds of publicly available supernova spectral time series. Radiative transfer modeling of this data gives insights into the physical properties of these explosions such as the composition, the density structure, or the intrinsic luminosity---this is invaluable for understanding the supernova progenitors, the explosion mechanism, or for constraining the supernova distance. However, a detailed parameter study of the available data has been out of reach due to the high dimensionality of the problem coupled with the still significant computational expense. We tackle this issue through the use of machine-learning emulators, which are algorithms for high-dimensional interpolation. These use a pre-calculated training dataset to mimic the output of a complex code but with run times orders of magnitude shorter. We present the application of such an emulator to synthetic type II supernova spectra generated with the TARDIS radiative transfer code. The results show that with a relatively small training set of 780 spectra we can generate emulated spectra with interpolation uncertainties of less than one percent. We demonstrate the utility of this method by automatic spectral fitting of two well-known type IIP supernovae; as an exemplary application, we determine the supernova distances from the spectral fits using the tailored-expanding-photosphere method. We compare our results to previous studies and find good agreement. This suggests that emulation of TARDIS spectra can likely be used to perform automatic and detailed analysis of many transient classes putting the analysis of large data repositories within reach.Comment: 18 pages, 13 figures, 3 tables, submitted to A&

Spectral modeling of type II supernovae

There are now hundreds of publicly available supernova spectral time series. Radiative transfer modeling of this data provides insight into the physical properties of these explosions, such as the composition, the density structure, and the intrinsic luminosity, which is invaluable for understanding the supernova progenitors, the explosion mechanism, and for constraining the supernova distance. However, a detailed parameter study of the available data has been out of reach due to the high dimensionality of the problem coupled with the still significant computational expense. We tackle this issue through the use of machine-learning emulators, which are algorithms for high-dimensional interpolation. These use a pre-calculated training dataset to mimic the output of a complex code but with run times that are orders of magnitude shorter. We present the application of such an emulator to synthetic type II supernova spectra generated with the TARDI