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Lorenz P.,University of Rostock | Hecker M.,Steinbeis Transfer Center for Proteome Analysis
Microchimica Acta | Year: 2014

The analysis of quantitative PCR data usually does not take into account the fact that the increase in fluorescence depends on the monitoring chemistry, the input of ds-DNA or ss-cDNA, and the directionality of the targeting of probes or primers. The monitoring chemistries currently available can be categorized into six groups: (A) DNA-binding dyes; (B) hybridization probes; (C) hydrolysis probes; (D) LUX primers; (E) hairpin primers; and (F) the QZyme system. We have determined the kinetics of the increase in fluorescence for each of these groups with respect to the input of both ds-DNA and ss-cDNA. For the latter, we also evaluated mRNA and cDNA targeting probes or primers. This analysis revealed three situations. Hydrolysis probes and LUX primers, compared to DNA-binding dyes, do not require a correction of the observed quantification cycle. Hybridization probes and hairpin primers require a correction of −1 cycle (dubbed C-lag), while the QZyme system requires the C-lag correction and an efficiency-dependent C-shift correction. A PCR efficiency value can be derived from the relative increase in fluorescence in the exponential phase of the amplification curve for all monitoring chemistries. In case of hydrolysis probes, LUX primers and hairpin primers, however, this should be performed after cycle 12, and for the QZyme system after cycle 19, to keep the overestimation of the PCR efficiency below 0.5 %. © 2014, The Author(s). Source

Wollbold J.,Steinbeis Transfer Center for Proteome Analysis | Huber R.,University Hospital Jena | Huber R.,Hannover Medical School | Kinne R.,University Hospital Jena | Wolff K.E.,FH Darmstadt
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

The present work visualizes and interprets gene expression data of arthritic patients using the mathematical theory of Formal Concept Analysis (FCA). For the purpose of representing gene expression processes we employ the branch of Temporal Concept Analysis (TCA) which has been introduced during the last ten years in order to support conceptual reasoning about temporal phenomena. In TCA, movements of general objects in abstract or "real" space and time can be described in a conceptual framework. For our purpose in this paper we only need a special case of the general notion of a Conceptual Semantic System (CSS), namely a Conceptual Time System with actual Objects and a Time relation (CTSOT). In the theory of CTSOTs, there are clear mathematical definitions of notions of objects, states, situations, transitions and life tracks. It is very important for our application that these notions are compatible with the granularity of the chosen scaling of the original data. This paper contributes to the biomedical study of disease processes in rheumatoid arthritis (RA) and the inflammatory disease control osteo-arthritis (OA), focusing on their molecular regulation. Time series of messenger RNA (mRNA) concentration levels in synovial cells from RA and OA patients were measured for a period of 12 hours after cytokine stimulation. These data are represented simultaneously as life tracks in transition diagrams of concept lattices constructed from the mRNA measurements for small sets of interesting genes. Biologically interesting differences between the two groups of patients are revealed. The transition diagrams are compared to literature and expert knowledge in order to explain the observed transitions by influences of certain proteins on gene transcription and to deduce new hypotheses concerning gene regulation. © 2011 Springer-Verlag. Source

Hecker M.,Steinbeis Transfer Center for Proteome Analysis | Hecker M.,University of Rostock | Thamilarasan M.,University of Rostock | Koczan D.,University of Rostock | And 6 more authors.
International Journal of Molecular Sciences | Year: 2013

MicroRNAs (miRNAs) are small non-coding RNA molecules acting as post-transcriptional regulators of gene expression. They are involved in many biological processes, and their dysregulation is implicated in various diseases, including multiple sclerosis (MS). Interferon-beta (IFN-beta) is widely used as a first-line immunomodulatory treatment of MS patients. Here, we present the first longitudinal study on the miRNA expression changes in response to IFN-beta therapy. Peripheral blood mononuclear cells (PBMC) were obtained before treatment initiation as well as after two days, four days, and one month, from patients with clinically isolated syndrome (CIS) and patients with relapsing-remitting MS (RRMS). We measured the expression of 651 mature miRNAs and about 19,000 mRNAs in parallel using real-time PCR arrays and Affymetrix microarrays. We observed that the up-regulation of IFN-beta-responsive genes is accompanied by a down-regulation of several miRNAs, including members of the mir-29 family. These differentially expressed miRNAs were found to be associated with apoptotic processes and IFN feedback loops. A network of miRNA-mRNA target interactions was constructed by integrating the information from different databases. Our results suggest that miRNA-mediated regulation plays an important role in the mechanisms of action of IFN-beta, not only in the treatment of MS but also in normal immune responses. miRNA expression levels in the blood may serve as a biomarker of the biological effects of IFN-beta therapy that may predict individual disease activity and progression. © 2013 by the authors; licensee MDPI, Basel, Switzerland. Source

Lustrek M.,University of Rostock | Lustrek M.,Jozef Stefan Institute | Lorenz P.,University of Rostock | Kreutzer M.,University of Rostock | And 15 more authors.
PLoS ONE | Year: 2013

Epitope-antibody-reactivities (EAR) of intravenous immunoglobulins (IVIGs) determined for 75,534 peptides by microarray analysis demonstrate that roughly 9% of peptides derived from 870 different human protein sequences react with antibodies present in IVIG. Computational prediction of linear B cell epitopes was conducted using machine learning with an ensemble of classifiers in combination with position weight matrix (PWM) analysis. Machine learning slightly outperformed PWM with area under the curve (AUC) of 0.884 vs. 0.849. Two different types of epitope-antibody recognitionmodes (Type I EAR and Type II EAR) were found. Peptides of Type I EAR are high in tyrosine, tryptophan and phenylalanine, and low in asparagine, glutamine and glutamic acid residues, whereas for peptides of Type II EAR it is the other way around. Representative crystal structures present in the Protein Data Bank (PDB) of Type I EAR are PDB 1TZI and PDB 2DD8, while PDB 2FD6 and 2J4W are typical for Type II EAR. Type I EAR peptides share predicted propensities for being presented by MHC class I and class II complexes. The latter interaction possibly favors T cell-dependent antibody responses including IgG class switching. Peptides of Type II EAR are predicted not to be preferentially presented by MHC complexes, thus implying the involvement of T cell-independent IgG class switch mechanisms. The high extent of IgG immunoglobulin reactivity with human peptides implies that circulating IgG molecules are prone to bind to human protein/peptide structures under non-pathological, non-inflammatory conditions. A webserver for predicting EAR of peptide sequences is available at www.sysmed-immun.eu/EAR. © 2013 Luštrek et al. Source

Hecker M.,Steinbeis Transfer Center for Proteome Analysis | Paap B.K.,University of Rostock | Goertsches R.H.,Steinbeis Transfer Center for Proteome Analysis | Kandulski O.,University of Rostock | And 5 more authors.
PLoS ONE | Year: 2011

Despite considerable advances in the treatment of multiple sclerosis, current drugs are only partially effective. Most patients show reduced disease activity with therapy, but still experience relapses, increasing disability, and new brain lesions. Since there are no reliable clinical or biological markers of disease progression, long-term prognosis is difficult to predict for individual patients. We identified 18 studies that suggested genes expressed in blood as predictive biomarkers. We validated the prognostic value of those genes with three different microarray data sets comprising 148 patients in total. Using these data, we tested whether the genes were significantly differentially expressed between patients with good and poor courses of the disease. Poor progression was defined by relapses and/or increase of disability during a two-year follow-up, independent of the administered therapy. Of 110 genes that have been proposed as predictive biomarkers, most could not be confirmed in our analysis. However, the G protein-coupled membrane receptor GPR3 was expressed at significantly lower levels in patients with poor disease progression in all data sets. GPR3 has therefore a high potential to be a biomarker for predicting future disease activity. In addition, we examined the IL17 cytokines and receptors in more detail and propose IL17RC as a new, promising, transcript-based biomarker candidate. Further studies are needed to better understand the roles of these receptors in multiple sclerosis and its treatment and to clarify the utility of GPR3 and IL17RC expression levels in the blood as markers of long-term prognosis. © 2011 Hecker et al. Source

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