InfectoGnostics Research Campus Jena

Jena, Germany

InfectoGnostics Research Campus Jena

Jena, Germany
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Schauer A.E.,Jena University Hospital | Klassert T.E.,Jena University Hospital | Von Lachner C.,Jena University Hospital | Riebold D.,InfectoGnostics Research Campus Jena | And 15 more authors.
Journal of Innate Immunity | Year: 2017

Streptococcus pneumoniae infections can lead to severe complications with excessive immune activation and tissue damage. Interleukin-37 (IL-37) has gained importance as a suppressor of innate and acquired immunity, and its effects have been therapeutic as they prevent tissue damage in autoimmune and inflammatory diseases. By using RAW macrophages, stably transfected with human IL-37, we showed a 70% decrease in the cytokine levels of IL-6, TNF-α, and IL-1β, and a 2.2-fold reduction of the intracellular killing capacity of internalized pneumococci in response to pneumococcal infection. In a murine model of infection with S. pneumoniae, using mice transgenic for human IL-37b (IL-37tg), we observed an initial decrease in cytokine expression of IL-6, TNF-α, and IL-1β in the lungs, followed by a late-phase enhancement of pneumococcal burden and subsequent increase of proinflammatory cytokine levels. Additionally, a marked increase in recruitment of alveolar macrophages and neutrophils was noted, while TRAIL mRNA was reduced 3-fold in lungs of IL-37tg mice, resulting in necrotizing pneumonia with augmented death of infiltrating neutrophils, enhanced bacteremic spread, and increased mortality. In conclusion, we have identified that IL-37 modulates several core components of a successful inflammatory response to pneumococcal pneumonia, which lead to increased inflammation, tissue damage, and mortality. © 2017 S. Karger AG, Basel.


Guo S.,Institute of Photonic Technology | Guo S.,Friedrich - Schiller University of Jena | Bocklitz T.,Institute of Photonic Technology | Bocklitz T.,Friedrich - Schiller University of Jena | And 6 more authors.
Analytical Methods | Year: 2017

The common mistakes of cross-validation (CV) for the development of chemometric models for Raman based biological applications were investigated. We focused on two common mistakes: the first mistake occurs when splitting the dataset into training and validation datasets improperly; and the second mistake is regarding the wrong position of a dimension reduction procedure with respect to the CV loop. For the first mistake, we split the dataset either randomly or each technical replicate was used as one fold of the CV and we compared the results. To check the second mistake, we employed two dimension reduction methods including principal component analysis (PCA) and partial least squares regression (PLS). These dimension reduction models were constructed either once for the whole training data outside the CV loop or rebuilt inside the CV loop for each iteration. We based our study on a benchmark dataset of Raman spectra of three cell types, which included nine technical replicates respectively. Two binary classification models were constructed with a two-layer CV. For the external CV, each replicate was used once as the independent testing dataset. The other replicates were used for the internal CV, where different methods of data splitting and different positions of the dimension reduction were studied. The conclusions include two points. The first point is related to the reliability of the model evaluation by the internal CV, illustrated by the differences between the testing accuracies from the external CV and the validation accuracies from the internal CV. It was demonstrated that the dataset should be split at the highest hierarchical level, which means the biological/technical replicate in this manuscript. Meanwhile, the dimension reduction should be redone for each iteration of the internal CV loop. The second point is the optimization of the performance of the internal CV, benchmarked by the prediction accuracy of the optimized model on the testing dataset. Comparable results were observed for different methods of data splitting and positions of dimension reduction in the internal CV. This means if the internal CV is used for optimizing the model parameters, the two mistakes are less influential in contrast to the model evaluation. © 2017 The Royal Society of Chemistry.


Lorenz B.,Friedrich - Schiller University of Jena | Lorenz B.,InfectoGnostics Research Campus Jena | Wichmann C.,InfectoGnostics Research Campus Jena | Wichmann C.,Institute of Photonic Technology | And 7 more authors.
Trends in Microbiology | Year: 2017

Raman spectroscopy is currently advertised as a hot and ambitious technology that has all of the features needed to characterize and identify bacteria. Raman spectroscopy is rapid, easy to use, noninvasive, and it could complement established microbiological and biomolecular methods in the near future. To bring this vision closer to reality, ongoing research is being conducted on spectral fingerprinting. This can yield a wealth of information, from even single bacteria from various habitats which can be further improved by combining Raman spectroscopy with methods such as stable isotope probing to elucidate microbial interactions. In conjunction with extensive statistical analysis, Raman spectroscopy will allow identification of (non)pathogenic bacteria at different taxonomic levels. The combination of a Raman setup with an optical microscope allows nondestructive detection of single bacterial cells.Since Raman spectroscopy is a phenotypic method, all relevant environmental factors or physiological states need to be taken into account for Raman databases.Raman microscopic databases enable the identification of the leading pathogen in environmental or patient samples.Raman-based stable isotope probing is developing into a powerful tool for following metabolic processes. © 2017 Elsevier Ltd.


Silge A.,Friedrich - Schiller University of Jena | Silge A.,InfectoGnostics Research Campus Jena | Abdou E.,Friedrich - Schiller University of Jena | Abdou E.,InfectoGnostics Research Campus Jena | And 16 more authors.
Cellular Microbiology | Year: 2015

Macrophages are the primary habitat of pathogenic mycobacteria during infections. Current research about the host-pathogen interaction on the cellular level is still going on. The present study proves the potential of Raman microspectroscopy as a label-free and non-invasive method to investigate intracellular mycobacteria in situ. Therefore, macrophages were infected with Mycobacterium gordonae, a mycobacterium known to cause inflammation linked to intracellular survival in macrophages. Here, we show that Raman maps provided spatial and spectral information about the position of bacteria within determined cell margins of macrophages in two-dimensional scans and in three-dimensional image stacks. Simultaneously, the relative intracellular concentration and distributions of cellular constituents such as DNA, proteins and lipids provided phenotypic information about the infected macrophages. Locations of bacteria outside or close to the outer membrane of the macrophages were notably different in their spectral pattern compared with intracellular once. Furthermore, accumulations of bacteria inside of macrophages exhibit distinct spectral/molecular information because of the chemical composition of the intracellular microenvironment. The data show that the connection of microscopically and chemically gained information provided by Raman microspectroscopy offers a new analytical way to detect and to characterize the mycobacterial infection of macrophages. © 2014 John Wiley & Sons Ltd.


Hidi I.J.,Friedrich - Schiller University of Jena | Hidi I.J.,Institute of Photonic Technology | Jahn M.,Friedrich - Schiller University of Jena | Jahn M.,Institute of Photonic Technology | And 8 more authors.
Journal of Physical Chemistry C | Year: 2016

The pharmacokinetics of antibiotics such as levofloxacin exhibits large interindividual differences, questioning the value of fixed dose regimens and warranting individual dosing based on therapeutic drug monitoring. Here, in a proof of principal study, it is shown that levofloxacin can be detected in human urine samples by employing lab-on-a-chip surface enhanced Raman spectroscopy (LoC-SERS). First, artificial urine is used as a matrix in order to get insights into the influence of different parameters such as matrix complexity, aggregation time, and matrix dilution on the overall SERS signal. Second, three anonymized individual and three pooled urine samples originating from patients undergoing either no or unknown medical treatments have been spiked with the target analyte. Measurements were performed with a benchtop and a portable Raman setup. In all six samples urinary levofloxacin concentrations between 0.45 mM (162.6 μg/mL) and 1.8 mM (650.5 μg/mL) have been successfully detected. According to the literature, the normal levofloxacin concentration in urine is 1.38 mM ± 0.68 mM with a minimum measured concentration of 0.45 mM after 4 h from the administration of a 500 mg dose. The presented results therefore show that LoC-SERS is a promising bioanalytical tool for urine analysis. © 2016 American Chemical Society.


Guo S.,Friedrich - Schiller University of Jena | Bocklitz T.,Friedrich - Schiller University of Jena | Bocklitz T.,InfectoGnostics Research Campus Jena | Popp J.,Friedrich - Schiller University of Jena | And 2 more authors.
Analyst | Year: 2016

In the last decade Raman-spectroscopy has become an invaluable tool for biomedical diagnostics. However, a manual rating of the subtle spectral differences between normal and abnormal disease states is not possible or practical. Thus it is necessary to combine Raman-spectroscopy with chemometrics in order to build statistical models predicting the disease states directly without manual intervention. Within chemometrical analysis a number of corrections have to be applied to receive robust models. Baseline correction is an important step of the pre-processing, which should remove spectral contributions of fluorescence effects and improve the performance and robustness of statistical models. However, it is demanding, time-consuming, and depends on expert knowledge to select an optimal baseline correction method and its parameters every time working with a new dataset. To circumvent this issue we proposed a genetic algorithm based method to automatically optimize the baseline correction. The investigation was carried out in three main steps. Firstly, a numerical quantitative marker was defined to evaluate the baseline estimation quality. Secondly, a genetic algorithm based methodology was established to search the optimal baseline estimation with the defined quantitative marker as evaluation function. Finally, classification models were utilized to benchmark the performance of the optimized baseline. For comparison, model based baseline optimization was carried out applying the same classifiers. It was proven that our method could provide a semi-optimal and stable baseline estimation without any chemical knowledge required or any additional spectral information used. © The Royal Society of Chemistry 2016.


Silge A.,Friedrich - Schiller University of Jena | Silge A.,InfectoGnostics Research Campus Jena | Schumacher W.,Friedrich - Schiller University of Jena | Schumacher W.,InfectoGnostics Research Campus Jena | And 7 more authors.
Systematic and Applied Microbiology | Year: 2014

The identification of Pseudomonas aeruginosa from samples of bottled natural mineral water by the analysis of subcultures is time consuming and other species of the authentic Pseudomonas group can be a problem. Therefore, this study aimed to investigate the influence of different aquatic environmental conditions (pH, mineral content) and growth phases on the cultivation-free differentiation between water-conditioned Pseudomonas spp. by applying Raman microspectroscopy. The final dataset was comprised of over 7500 single-cell Raman spectra, including the species Pseudomonas aeruginosa, P. fluorescens and P. putida, in order to prove the feasibility of the introduced approach. The collection of spectra was standardized by automated measurements of viable stained bacterial cells. The discrimination was influenced by the growth phase at the beginning of the water adaptation period and by the type of mineral water. Different combinations of the parameters were tested and they resulted in accuracies of up to 85% for the identification of P. aeruginosa from independent samples by applying chemometric analysis. © 2014 Elsevier GmbH.


Pahlow S.,Friedrich - Schiller University of Jena | Pahlow S.,InfectoGnostics Research Campus Jena | Meisel S.,Friedrich - Schiller University of Jena | Meisel S.,InfectoGnostics Research Campus Jena | And 11 more authors.
Advanced Drug Delivery Reviews | Year: 2015

Bacterial detection is a highly topical research area, because various fields of application will benefit from the progress being made. Consequently, new and innovative strategies which enable the investigation of complex samples, like body fluids or food stuff, and improvements regarding the limit of detection are of general interest. Within this review the prospects of Raman spectroscopy as a reliable tool for identifying bacteria in complex samples are discussed.The main emphasis of this work is on important aspects of applying Raman spectroscopy for the detection of bacteria like sample preparation and the identification process. Several approaches for a Raman compatible isolation of bacterial cells have been developed and applied to different matrices. Here, an overview of the limitations and possibilities of these methods is provided. Furthermore, the utilization of Raman spectroscopy for diagnostic purposes, food safety and environmental issues is discussed under a critical view. © 2015 Elsevier B.V.


Monecke S.,TU Dresden | Monecke S.,Alere Technologies GmbH | Monecke S.,Infectognostics Research Campus Jena | Engelmann I.,Alere Technologies GmbH | And 3 more authors.
Molecular and Cellular Probes | Year: 2015

Spoligotyping is a widely used typing method for the Mycobacterium tuberculosis complex. Protocols and platforms can be adapted for direct use on patient samples. Serial dilutions of genomic DNA from Mycobacterium bovis BCG strain DSM45071 were spoligotyped by array hybridization using 32 different commercial PCR polymerase preparations. In samples with very low concentrations of mycobacterial DNA, commercially available PCR polymerases differed in their performance, and some yielded no, or false, identification. Direct spoligotyping from samples with very low concentrations of mycobacterial DNA thus requires careful selection of polymerase and strict standardization. © 2015 Elsevier Ltd.


Liebe S.,Institute of Sugar Beet Research | Christ D.S.,Institute of Sugar Beet Research | Ehricht R.,Alere Technologies GmbH | Ehricht R.,InfectoGnostics Research Campus Jena | Varrelmann M.,Institute of Sugar Beet Research
Phytopathology | Year: 2016

Sugar beet root rot diseases that occur during the cropping season or in storage are accompanied by high yield losses and a severe reduction of processing quality. The vast diversity of microorganism species involved in rot development requires molecular tools allowing simultaneous identification of many different targets. Therefore, a new microarray technology (ArrayTube) was applied in this study to improve diagnosis of sugar beet root rot diseases. Based on three marker genes (internal transcribed spacer, translation elongation factor 1 alpha, and 16S ribosomal DNA), 42 well-performing probes enabled the identification of prevalent field pathogens (e.g., Aphanomyces cochlioides), storage pathogens (e.g., Botrytis cinerea), and ubiquitous spoilage fungi (e.g., Penicillium expansum). All probes were proven for specificity with pure cultures from 73 microorganism species as well as for in planta detection of their target species using inoculated sugar beet tissue. Microarray-based identification of root rot pathogens in diseased field beets was successfully confirmed by classical detection methods. The high discriminatory potential was proven by Fusarium species differentiation based on a single nucleotide polymorphism. The results demonstrate that the ArrayTube constitute an innovative tool allowing a rapid and reliable detection of plant pathogens particularly when multiple microorganism species are present. © 2016 The American Phytopathological Society.

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