Biomedical subjects

Eva Möller

Publications and source records attributed to Eva Möller.

5 recordsLinked to original sources

PubMed2006

Platelet-derived microvesicles induce differential gene expression in monocytic cells: a DNA microarray study.

Platelet-derived microvesicles (PMV) that are shed from the plasma membrane of activated platelets, expose various platelet-type antigens on their surface and are able to adhere to other blood cells and endothelial cells. There are several clinical conditions with markedly increased numbers of PMV, e.g. acute coronary syndrome, thrombotic microangiopathy and sepsis. To prove whether PMV may contribute to an inflammatory response we used DNA microarray technology to study the effect of PMV on gene expression in the prototypic monocytic cell line MonoMac 6 (MM6). PMV were generated by activating human platelets in plasma with collagen and subsequent removal of platelets and plasma by repeated centrifugation. MM6 were incubated for 2 h with PMV in a ratio corresponding to 75 platelets/cell, or saline as control. After RNA isolation, reverse transcription and fluorescence labelling, cDNA was hybridized on a medium density microarray comprising 5308 probes addressing 4868 transcripts of 4730 human genes relevant to inflammation, immune response and related processes. The formation of PMV-MM6 conjugates was associated with significant variations in gene expression, i.e. 93 genes were found to be differentially expressed (P < 0.001; q < 0.087). Among them, 47 genes with annotated transcripts and proteins were identified. Using Ingenuity Pathway Analysis, 37 of the differentially expressed genes were identified as parts of networks associated with functional pathways including cell-to-cell signalling, cellular growth and proliferation, regulation of gene expression and lipid metabolism. For sphingosine kinase-1 the increased expression could be confirmed exemplarily not only by RT-PCR but also on the enzyme activity level. The data indicate that PMV signal differential expression of inflammation-relevant genes in monocytic cells and may represent a novel link between hemostasis and inflammation.

Blood Platelets↗

PubMed2005

Transcription in response to physical stress--clues to the molecular mechanisms of exercise-induced asthma.

To clarify stress-induced immunological reactions and molecular events during exercise and the potential relevance to exercise-induced bronchoconstriction, transcriptional responses to standardized physical stress were determined. Six healthy, young volunteers underwent an endurance exercise of 90% of their individual anaerobic threshold for 90 min. Time-dependent alterations in the expression pattern of leukocytes from healthy, trained subjects were analyzed by DNA microarrays before and 2 h and 6 h after exercise. Starting out from a large collection of cDNA library clones comprising more than 70,000 human expressed sequence tags, we selected, designed, and immobilized oligonucleotide probes (60-70mers) for transcripts of 5000 stress- and inflammation-relevant genes. Exercise-induced stress provoked changes in the expression of 433 gene activities 2 h and/or 6 h after exercise, which could be grouped into six clusters. The most prominent feature was an enhanced transcription of two genes, coding for 5-lipoxygenase (ALOX5) and ALOX5-activating protein. Moreover, enhanced levels of leukotriene B4 (LTB4) and LTC4 (P<0.05) were detected in plasma after exercise. Our data demonstrate that exercise alters the activities of a distinct number of genes. In particular, they possibly provide novel insights into the molecular mechanisms of exercise-induced bronchoconstriction and suggest that enhanced transcription of ALOX5 and its activating protein together with a present predisposition of the subject critically contribute to exercise-induced asthma.

5-Lipoxygenase-Activating Proteins↗

PubMed2004

Expression profiling: toward an application in sepsis diagnostics.

Sepsis is a common and serious health problem whereby improvements in diagnosis are crucial in increasing survival rates. To test whether profiling transcription is applicable to sepsis diagnosis, we analyzed whole blood using a microarray containing probes for 340 genes relevant to inflammation. The patient's gene expression pattern was highly homogenous, resulting in 69% of differentially expressed genes. With a positive predictive value of 98%, a list of 50 differentially expressed genes was compiled, and randomly chosen transcripts were confirmed by PCR. Here, we present the first evidence that microarrays can identify typical gene expression profiles in the blood of patients with severe sepsis. Regardless of the heterogeneity of the patients, we observed a striking correlation between the conventional diagnostic classification and our approach. The unity of responses suggests that the principle of this multiparameter approach can be adapted to early stage sepsis diagnosis.

APACHE↗

PubMed2003

Fitting of one ARMA model to multiple trials increases the time resolution of instantaneous coherence.

This study presents a least mean squares (LMS) algorithm for the ensemble modeling of a multivariate ARMA process. Generally, an LMS algorithm makes possible the tracking of parameters for nonstationary time series. Our estimation incorporates multiple process observations that improve the accuracy of the parameter estimation. As a consequence, the estimation sequences come close to the true model parameters with a fast adaptation speed. This advantage also holds true of spectral quantities (e.g., the momentary coherence), which are derived from the model parameters. Thus the extension of the ARMA fitting from one to multiple trajectories allows the investigation of nonstationary biological signals with an increased time resolution. The applicability of the algorithm is demonstrated for event-related EEG coherence analysis of the Sternberg task. The changing interaction between posterior association cortex and anterior brain area was shown for verbal and nonverbal stimuli by means of the time-variant theta coherence.

Electroencephalography↗

PubMed2003

The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies.

Understanding of brain functioning requires the investigation of activated cortical networks, in particular the detection of interactions between different cortical sites. Commonly, coherence and correlation are used to describe interrelations between EEG signals. However, on this basis, no statements on causality or the direction of their interrelations are possible. Causality between two signals may be expressed in terms of upgrading the predictability of one signal by the knowledge of the immediate past of the other signal. The best-established approach in this context is the so-called Granger causality. The classical estimation of Granger causality requires the stationarity of the signals. In this way, transient pathways of information transfer stay hidden. The study presents an adaptive estimation of Granger causality. Simulations demonstrate the usefulness of the time-variant Granger causality for detecting dynamic causal relations within time intervals of less than 100 ms. The time-variant Granger causality is applied to EEG data from the Stroop task. It was shown that conflict situations generate dense webs of interactions directed from posterior to anterior cortical sites. The web of directed interactions occurs mainly 400 ms after the stimulus onset and lasts up to the end of the task.

Adult↗