TY - JOUR A1 - Marehn, David Thomas A1 - Wilhelm, Detlef A1 - Pospisil, Heike A1 - Pizzoferrato, Roberto T1 - Double entry method for the verification of data a chromatography data system receives JF - Journal of Sensors and Sensor Systems N2 - The importance of software validation increases since the need for high usability and suitability of software applications grows. In order to reduce costs and manage risk factors, more and more recommendations and rules have been established. In the field of pharmacy the vendors of so-called chromatography data systems (CDSs) had to implement the guidelines of the Code of Federal Regulations Title 21 (CFR 21) during the last few years in order to fulfill the increasing requirements. The CFR 21 part 11 deals with electronic records and signatures. This part is binding for each company in the regulated environment that wishes to create, edit and sign electronic information instead of printing them on paper. Subsection CFR 21 part 11.10(h) explains how to perform an input check for manual user entries as well as for data that will be collected from an external device. In this article we present an approach performing the double entry method on data provided by the hardware instrument in order to investigate possible influences on the raw data by the handling CDS. A software tool has been written which allows us to communicate with a high-performance liquid chromatography (HPLC) detector and acquire data from it. The communication is completely independent of a CDS which is started separately and connected to the same system. Using this configuration we made a parallel data acquisition of two instances at the same time possible. Two CDSs have been tested and for at least one of them it has been shown that a comparison of the acquired data can be done as with the double entry method for the data verification. For the second CDS we checked whether it would be applicable after a few modifications. The given approach could be either used for a live data verification of produced raw data or as a single test during a software operational qualification to verify the data acquisition functionality of the software. Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-12349 SN - 2194-878X VL - 8 SP - 207 EP - 214 ER - TY - JOUR A1 - Balabanov, Stefan A1 - Wilhelm, Thomas A1 - Venz, Simone A1 - Keller, Gunhild A1 - Scharf, Christian A1 - Pospisil, Heike A1 - Braig, Melanie A1 - Barett, Christine A1 - Bokemeyer, Carsten A1 - Walther, Reinhard A1 - Brümmendorf, Tim H. A1 - Schuppert, Andreas T1 - Combination of a Proteomics Approach and Reengineering of Meso Scale Network Models for Prediction of Mode-of-Action for Tyrosine Kinase Inhibitors JF - PLoS ONE N2 - In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs. Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6370 SN - 1932-6203 VL - 8 IS - 1 ER - TY - JOUR A1 - Thiemann, Alexander A1 - Fu, Junjie A1 - Seifert, Felix A1 - Grant-Downton, Robert T A1 - Schrag, Tobias A A1 - Pospisil, Heike A1 - Frisch, Matthias A1 - Melchinger, Albrecht E A1 - Scholten, Stefan T1 - Genome-wide meta-analysis of maize heterosis reveals the potential role of additive gene expression at pericentromeric loci JF - BMC Plant Biology N2 - The identification of QTL involved in heterosis formation is one approach to unravel the not yet fully understood genetic basis of heterosis - the improved agronomic performance of hybrid F1 plants compared to their inbred parents. The identification of candidate genes underlying a QTL is important both for developing markers and determining the molecular genetic basis of a trait, but remains difficult owing to the large number of genes often contained within individual QTL. To address this problem in heterosis analysis, we applied a meta-analysis strategy for grain yield (GY) of Zea mays L. as example, incorporating QTL-, hybrid field-, and parental gene expression data. KW - heterosis KW - maize KW - QTL KW - grain yield KW - additive gene expression Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-5999 SN - 1471-2229 VL - 14 IS - 88 ER - TY - JOUR A1 - Ummanni, Ramesh A1 - Mundt, Frederike A1 - Pospisil, Heike A1 - Venz, Simone A1 - Scharf, Christian A1 - Barett, Christine A1 - Fälth, Maria A1 - Köllermann, Jens A1 - Walther, Reinhard A1 - Schlomm, Thorsten A1 - Sauter, Guido A1 - Bokemeyer, Carsten A1 - Sültmann, Holger A1 - Schuppert, Andreas A1 - Brümmendorf, Tim H. A1 - Balabanov, Stefan T1 - Identification of Clinically Relevant Protein Targets in Prostate Cancer with 2D-DIGE Coupled Mass Spectrometry and Systems Biology Network Platform JF - PLoS ONE N2 - Prostate cancer (PCa) is the most common type of cancer found in men and among the leading causes of cancer death in the western world. In the present study, we compared the individual protein expression patterns from histologically characterized PCa and the surrounding benign tissue obtained by manual micro dissection using highly sensitive two-dimensional differential gel electrophoresis (2D-DIGE) coupled with mass spectrometry. Proteomic data revealed 118 protein spots to be differentially expressed in cancer (n = 24) compared to benign (n = 21) prostate tissue. These spots were analysed by MALDI-TOF-MS/MS and 79 different proteins were identified. Using principal component analysis we could clearly separate tumor and normal tissue and two distinct tumor groups based on the protein expression pattern. By using a systems biology approach, we could map many of these proteins both into major pathways involved in PCa progression as well as into a group of potential diagnostic and/or prognostic markers. Due to complexity of the highly interconnected shortest pathway network, the functional sub networks revealed some of the potential candidate biomarker proteins for further validation. By using a systems biology approach, our study revealed novel proteins and molecular networks with altered expression in PCa. Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies. Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6432 SN - 1932-6203 VL - 6 IS - 2 ER - TY - JOUR A1 - Standfuß, Christoph A1 - Pospisil, Heike A1 - Klein, Andreas T1 - SNP microarray analyses reveal copy number alterations and progressive genome reorganization during tumor development in SVT/t driven mice breast cancer JF - BMC Cancer N2 - Tumor development is known to be a stepwise process involving dynamic changes that affect cellular integrity and cellular behavior. This complex interaction between genomic organization and gene, as well as protein expression is not yet fully understood. Tumor characterization by gene expression analyses is not sufficient, since expression levels are only available as a snapshot of the cell status. So far, research has mainly focused on gene expression profiling or alterations in oncogenes, even though DNA microarray platforms would allow for high-throughput analyses of copy number alterations (CNAs). KW - breast cancer KW - genome reorganization KW - copy number alteration KW - CNV KW - fragile site Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6414 SN - 1471-2407 VL - 12 IS - 380 ER - TY - JOUR A1 - Klein, Andreas A1 - Pospisil, Heike T1 - Gene Expression Profiling of Pancreatic Cancer Reveals a Significant Deregulation of the TGF-β Pathway and the Discovery of Genes for Prognosis JF - Global Journal of Human Genetics & Gene Therapy N2 - We have re-analyzed previously published gene expression data from ninety-four pancreatic ductal adenocarcinomas (PDAC) samples. We determined the gene expression profile of genes differentially expressed in PDAC compared to non-malignant pancreatic tissue. Using the 100 top-ranked genes, we were able to discriminate between PDAC and non-malignant pancreatic tissue. A hierarchical cluster analysis revealed only a 6 % false discovery rate. The prognostic strength of these discriminative genes was underscored by a SVM classification and 3-fold cross validation with an 89 % correct class assignment. The annotation of the 100 top-ranked genes revealed that most of the genes were involved in the processes of signal transduction, cell adhesion, extracellular matrix organization and cell migration. The most greatly affected signal cascade was the transforming growth factor β receptor signaling pathway, which was significantly enriched in the top-ranked genes. Furthermore, we identified eleven genes that were associated with good prognosis. KW - pancreatic ductal adenocarcinoma KW - gene expression profiling KW - TGF-β Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6519 SN - 2311-0309 VL - 2 IS - 1 SP - 30 EP - 39 ER - TY - JOUR A1 - Hannemann, Juliane A1 - Meyer-Staeckling, Sönke A1 - Kemming, Dirk A1 - Alpers, Iris A1 - Joosse, Simon A. A1 - Pospisil, Heike A1 - Kurtz, Stefan A1 - Görndt, Jennifer A1 - Püschel, Klaus A1 - Riethdorf, Sabine A1 - Pantel, Klaus A1 - Brandt, Burkhard T1 - Quantitative High-Resolution Genomic Analysis of Single Cancer Cells JF - PLoS ONE N2 - During cancer progression, specific genomic aberrations arise that can determine the scope of the disease and can be used as predictive or prognostic markers. The detection of specific gene amplifications or deletions in single blood-borne or disseminated tumour cells that may give rise to the development of metastases is of great clinical interest but technically challenging. In this study, we present a method for quantitative high-resolution genomic analysis of single cells. Cells were isolated under permanent microscopic control followed by high-fidelity whole genome amplification and subsequent analyses by fine tiling array-CGH and qPCR. The assay was applied to single breast cancer cells to analyze the chromosomal region centred by the therapeutical relevant EGFR gene. This method allows precise quantitative analysis of copy number variations in single cell diagnostics. Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6426 SN - 1932-6203 VL - 6 IS - 11 ER - TY - JOUR A1 - Silbermann, Jascha A1 - Wernicke, Catrin A1 - Pospisil, Heike A1 - Frohme, Marcus T1 - RefPrimeCouch—a reference gene primer CouchApp JF - Database: The Journal of Biological Databases and Curation N2 - To support a quantitative real-time polymerase chain reaction standardization project, a new reference gene database application was required. The new database application was built with the explicit goal of simplifying not only the development process but also making the user interface more responsive and intuitive. To this end, CouchDB was used as the backend with a lightweight dynamic user interface implemented client-side as a one-page web application. Data entry and curation processes were streamlined using an OpenRefine-based workflow. The new RefPrimeCouch database application provides its data online under an Open Database License. Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6330 SN - 1758-0463 VL - 2013 ER - TY - JOUR A1 - Duscharla, Divya A1 - Bhumireddy, Sudarshana Reddy A1 - Lakshetti, Sridhar A1 - Pospisil, Heike A1 - Murthy, P. V. L. N. A1 - Walther, Reinhard A1 - Sripadi, Prabhakar A1 - Ummanni, Ramesh T1 - Prostate Cancer Associated Lipid Signatures in Serum Studied by ESI-Tandem Mass Spectrometryas Potential New Biomarkers JF - PLoS ONE N2 - Prostate cancer (PCa) is one amongst the most common cancersin western men. Incidence rate ofPCa is on the rise worldwide. The present study deals with theserum lipidome profiling of patients diagnosed with PCa to identify potential new biomarkers. We employed ESI-MS/MS and GC-MS for identification of significantly altered lipids in cancer patient’s serum compared to controls. Lipidomic data revealed 24 lipids are significantly altered in cancer patinet’s serum (n = 18) compared to normal (n = 18) with no history of PCa. By using hierarchical clustering and principal component analysis (PCA) we could clearly separate cancer patients from control group. Correlation and partition analysis along with Formal Concept Analysis (FCA) have identified that PC (39:6) and FA (22:3) could classify samples with higher certainty. Both the lipids, PC (39:6) and FA (22:3) could influence the cataloging of patients with 100% sensitivity (all 18 control samples are classified correctly) and 77.7% specificity (of 18 tumor samples 4 samples are misclassified) with p-value of 1.612×10−6 in Fischer’s exact test. Further, we performed GC-MS to denote fatty acids altered in PCa patients and found that alpha-linolenic acid (ALA) levels are altered in PCa. We also performed an in vitro proliferation assay to determine the effect of ALA in survival of classical human PCa cell lines LNCaP and PC3. We hereby report that the altered lipids PC (39:6) and FA (22:3) offer a new set of biomarkers in addition to the existing diagnostic tests that could significantly improve sensitivity and specificity in PCa diagnosis. Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-5863 SN - 1932-6203 VL - 11 IS - 3 ER - TY - JOUR A1 - Seifert, Felix A1 - Thiemann, Alexander A1 - Pospisil, Heike A1 - Scholten, Stefan T1 - Re-annotation of the maize oligonucleotide array JF - Maydica N2 - The microarray technology has become an established approach for large-scale gene expression analysis with mature protocols for sample, microarray, and data processing. The maize oligonucleotide array (maizearray) is one of the few microarray platforms designed for genome-wide gene expression analysis in Zea mays L. Many datas-ets addressing various genetic, physiological and developmental topics generated with this platform are available. The original 57,452 microarray probes were compiled based on expressed sequence tags (ESTs). Meanwhile the maize genome sequence became available providing the possibility for an improved annotation of the microar-ray probe set. In this study we determined the genome positions of all maizearray probes to obtain current gene annotations and generated current Gene Ontology (GO) annotations. These new data allow tracing redundancy of the probe set and interfering cross-hybridizations, and doubled the number of genes with functional GO data. Our re-annotation will largely improve the functional analysis of available and future datasets generated on this microarray platform. KW - maize oligonucleotide array KW - microarray KW - annotation Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6178 UR - https://journals-crea.4science.it/index.php/maydica/article/view/628 SN - 0025-6153 VL - 57 IS - 1 SP - 49 EP - 55 ER - TY - JOUR A1 - Marczok, Stefanie A1 - Bortz, Birgit A1 - Wang, Chong A1 - Pospisil, Heike T1 - Comprehensive Analysis of Genome Rearrangements in Eight Human Malignant Tumor Tissues JF - PLoS ONE N2 - Carcinogenesis is a complex multifactorial, multistage process, but the precise mechanisms are not well understood. In this study, we performed a genome-wide analysis of the copy number variation (CNV), breakpoint region (BPR) and fragile sites in 2,737 tumor samples from eight tumor entities and in 432 normal samples. CNV detection and BPR identification revealed that BPRs tended to accumulate in specific genomic regions in tumor samples whereas being dispersed genome-wide in the normal samples. Hotspots were observed, at which segments with similar alteration in copy number were overlapped along with BPRs adjacently clustered. Evaluation of BPR occurrence frequency showed that at least one was detected in about and more than 15% of samples for each tumor entity while BPRs were maximal in 12% of the normal samples. 127 of 2,716 tumor-relevant BPRs (termed ‘common BPRs’) exhibited also a noticeable occurrence frequency in the normal samples. Colocalization assessment identified 20,077 CNV-affecting genes and 169 of these being known tumor-related genes. The most noteworthy genes are KIAA0513 important for immunologic, synaptic and apoptotic signal pathways, intergenic non-coding RNA RP11-115C21.2 possibly acting as oncogene or tumor suppressor by changing the structure of chromatin, and ADAM32 likely importance in cancer cell proliferation and progression by ectodomain-shedding of diverse growth factors, and the well-known tumor suppressor gene p53. The BPR distributions indicate that CNV mutations are likely non-random in tumor genomes. The marked recurrence of BPRs at specific regions supports common progression mechanisms in tumors. The presence of hotspots together with common BPRs, despite its small group size, imply a relation between fragile sites and cancer-gene alteration. Our data further suggest that both protein-coding and non-coding genes possessing a range of biological functions might play a causative or functional role in tumor biology. This research enhances our understanding of the mechanisms for tumorigenesis and progression. Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6607 SN - 1932-6203 VL - 11 IS - 7 ER - TY - JOUR A1 - Pfeil, Juliane A1 - Siptroth, Julienne A1 - Pospisil, Heike A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Moskalenko, Olga A1 - Yateem, Murad A1 - Nechyporenko, Alina T1 - Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition JF - Big Data and Cognitive Computing N2 - Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera. KW - human intestinal microbiome KW - next-generation sequencing KW - type 2 diabetes KW - deep learning KW - image classification Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17184 SN - 2504-2289 VL - 7 IS - 1 PB - MDPI ER - TY - JOUR A1 - Siptroth, Julienne A1 - Moskalenko, Olga A1 - Krumbiegel, Carsten A1 - Ackermann, Jörg A1 - Koch, Ina A1 - Pospisil, Heike T1 - Variation of butyrate production in the gut microbiome in type 2 diabetes patients JF - International Microbiology N2 - Background Diabetes mellitus type 2 is a common disease that poses a challenge to the healthcare system. The disease is very often diagnosed late. A better understanding of the relationship between the gut microbiome and type 2 diabetes can support early detection and form an approach for therapies. Microbiome analysis offers a potential opportunity to find markers for this disease. Next-generation sequencing methods can be used to identify the bacteria present in the stool sample and to generate a microbiome profile through an analysis pipeline. Statistical analysis, e.g., using Student’s t-test, allows the identification of significant differences. The investigations are not only focused on single bacteria, but on the determination of a comprehensive profile. Also, the consideration of the functional microbiome is included in the analyses. The dataset is not from a clinical survey, but very extensive. Results By examining 946 microbiome profiles of diabetes mellitus type 2 sufferers (272) and healthy control persons (674), a large number of significant genera (25) are revealed. It is possible to identify a large profile for type 2 diabetes disease. Furthermore, it is shown that the diversity of bacteria per taxonomic level in the group of persons with diabetes mellitus type 2 is significantly reduced compared to a healthy control group. In addition, six pathways are determined to be significant for type 2 diabetes describing the fermentation to butyrate. These parameters tend to have high potential for disease detection. Conclusions With this investigation of the gut microbiome of persons with diabetes type 2 disease, we present significant bacteria and pathways characteristic of this disease. KW - NGS KW - type 2 diabetes KW - gut KW - butyrate KW - statistical analysis Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17802 VL - 26 SP - 601 EP - 610 PB - Springer Nature ER - TY - JOUR A1 - Siptroth, Julienne A1 - Moskalenko, Olga A1 - Krumbiegel, Carsten A1 - Ackermann, Jörg A1 - Koch, Ina A1 - Pospisil, Heike T1 - Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks JF - Discover Artificial Intelligence N2 - Background Type 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients. Methods Pathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways. Results It could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674). Conclusion With this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the basis. KW - type 2 diabetes mellitus KW - gut KW - NGS KW - artificial neural network KW - machine learning KW - explainable artificial intelligence (XAI) Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17497 SN - 2731-0809 VL - 3 IS - 1 PB - Springer Nature ER -