GeneSet2miRNA: finding the signature of cooperative miRNA activities in the gene lists

GeneSet2miRNA is the first web-based tool which is able to identify whether or not a gene list has a signature of miRNA-regulatory activity. As input, GeneSet2miRNA accepts a list of genes. As output, a list of miRNA-regulatory models is provided. A miRNA-regulatory model is a group of miRNAs (single, pair, triplet or quadruplet) that is predicted to regulate a significant subset of genes from the submitted list. GeneSet2miRNA provides a user friendly dialog-driven web page submission available for several model organisms. GeneSet2miRNA is freely available at http://mips.helmholtz-muenchen.de/proj/gene2mir/

Intronic microRNAs support their host genes by mediating synergistic and antagonistic regulatory effects

<p>Abstract</p> <p>Background</p> <p>MicroRNA-mediated control of gene expression via translational inhibition has substantial impact on cellular regulatory mechanisms. About 37% of mammalian microRNAs appear to be located within introns of protein coding genes, linking their expression to the promoter-driven regulation of the host gene. In our study we investigate this linkage towards a relationship beyond transcriptional co-regulation.</p> <p>Results</p> <p>Using measures based on both annotation and experimental data, we show that intronic microRNAs tend to support their host genes by regulation of target gene expression with significantly correlated expression patterns. We used expression data of three differentiating cell types and compared gene expression profiles of host and target genes. Many microRNA target genes show expression patterns significantly correlated with the expressions of the microRNA host genes. By calculating functional similarities between host and predicted microRNA target genes based on GO annotations, we confirm that many microRNAs link host and target gene activity in an either synergistic or antagonistic manner.</p> <p>Conclusions</p> <p>These two regulatory effects may result from fine tuning of target gene expression functionally related to the host or knock-down of remaining opponent target gene expression. This finding allows to extend the common practice of mapping large scale gene expression data to protein associated genes with functionality of co-expressed intronic microRNAs.</p

Analyzing M-CSF dependent monocyte/macrophage differentiation: Expression modes and meta-modes derived from an independent component analysis

<p>Abstract</p> <p>Background</p> <p>The analysis of high-throughput gene expression data sets derived from microarray experiments still is a field of extensive investigation. Although new approaches and algorithms are published continuously, mostly conventional methods like hierarchical clustering algorithms or variance analysis tools are used. Here we take a closer look at independent component analysis (ICA) which is already discussed widely as a new analysis approach. However, deep exploration of its applicability and relevance to concrete biological problems is still missing. In this study, we investigate the relevance of ICA in gaining new insights into well characterized regulatory mechanisms of M-CSF dependent macrophage differentiation.</p> <p>Results</p> <p>Statistically independent gene expression modes (GEM) were extracted from observed gene expression signatures (GES) through ICA of different microarray experiments. From each GEM we deduced a group of genes, henceforth called <it>sub-mode</it>. These <it>sub-modes </it>were further analyzed with different database query and literature mining tools and then combined to form so called <it>meta-modes</it>. With them we performed a knowledge-based pathway analysis and reconstructed a well known signal cascade.</p> <p>Conclusion</p> <p>We show that ICA is an appropriate tool to uncover underlying biological mechanisms from microarray data. Most of the well known pathways of M-CSF dependent monocyte to macrophage differentiation can be identified by this unsupervised microarray data analysis. Moreover, recent research results like the involvement of proliferation associated cellular mechanisms during macrophage differentiation can be corroborated.</p

Molecular classification of the placebo effect in nausea

In this proof-of-concept study, we tested whether placebo effects can be monitored and predicted by plasma proteins. In a randomized controlled design, 90 participants were exposed to a nauseating stimulus on two separate days and were randomly allocated to placebo treatment or no treatment on the second day. Significant placebo effects on nausea, motion sickness, and (in females) gastric activity could be verified. Using label-free tandem mass spectrometry, 74 differentially regulated proteins were identified as correlates of the placebo effect. Gene ontology (GO) enrichment analyses identified acute-phase proteins and microinflammatory proteins to be involved, and the identified GO signatures predicted day-adjusted scores of nausea indices in the placebo group. We also performed GO enrichment analyses of specific plasma proteins predictable by the experimental factors or their interactions and identified 'grooming behavior' as a prominent hit. Finally, Receiver Operator Characteristics (ROC) allowed to identify plasma proteins differentiating placebo responders from non-responders, comprising immunoglobulins and proteins involved in oxidation reduction processes and complement activation. Plasma proteomics is a promising tool to identify molecular correlates and predictors of the placebo effect in humans

Improvement of renal function after transcatheter aortic valve replacement and its impact on survival

Background Chronic kidney disease as well as acute kidney injury are associated with adverse outcomes after transcatheter aortic valve replacement (TAVR). However, little is known about the prognostic implications of an improvement in renal function after TAVR. Methods Renal improvement (RI) was defined as a decrease in postprocedural creatinine in μmol/l of ≥1% compared to its preprocedural baseline value. A propensity score representing the likelihood of RI was calculated to define patient groups which were comparable regarding potential confounders (age, sex, BMI, NYHA classification, STS score, log. EuroSCORE, history of atrial fibrillation/atrial flutter, pulmonary disease, previous stroke, CRP, creatinine, hsTNT and NT-proBNP). The cohort was stratified into 5 quintiles according to this propensity score and the survival time after TAVR was compared within each subgroup. Results Patients in quintile 5 (n = 93) had the highest likelihood for RI. They were characterized by higher creatinine, lower eGFR, higher NYHA class, higher NT-proBNP, being mostly female and having shorter overall survival time. Within quintile 5, patients without RI had significantly shorter survival compared to patients with RI (p = 0.002, HR = 0.32, 95% CI = [0.15-0.69]). There was no survival time difference between patients with and without RI in the whole cohort (p = 0.12) and in quintiles 1 to 4 (all p > 0.16). Analyses of specific subgroups showed that among patients with NYHA class IV, those with RI also had a significant survival time benefit (p < 0.001, HR = 0.15; 95%-CI = [0.05-0.44]) compared to patients without RI. Conclusions We here describe a propensity score-derived specific subgroup of patients in which RI after TAVR correlated with a significant survival benefit

The 2010 Signal Separation Evaluation Campaign (SiSEC2010): - Biomedical source separation -

International audienceWe present an overview of the biomedical part of the 2010 community-based Signal Separation Evaluation Campaign (SiSEC2010), coordinated by the authors. In addition to the audio tasks which have been evaluated in the previous SiSEC, SiSEC2010 considered several biomedical tasks. Here, three biomedical datasets from molecular biology (gene expression profiles) and neuroscience (EEG) were contributed. This paper describes the biomedical datasets, tasks and evaluation criteria. This paper also reports the results of the biomedical part of SiSEC2010 achieved by participants

Adipocyte-derived extracellular vesicles increase insulin secretion through transport of insulinotropic protein cargo

Adipocyte-derived extracellular vesicles (AdEVs) are membranous nanoparticles that convey communication from adipose tissue to other organs. Here, to delineate their role as messengers with glucoregulatory nature, we paired fluorescence AdEV-tracing and SILAC-labeling with (phospho)proteomics, and revealed that AdEVs transfer functional insulinotropic protein cargo into pancreatic β-cells. Upon transfer, AdEV proteins were subjects for phosphorylation, augmented insulinotropic GPCR/cAMP/PKA signaling by increasing total protein abundances and phosphosite dynamics, and ultimately enhanced 1st-phase glucose-stimulated insulin secretion (GSIS) in murine islets. Notably, insulinotropic effects were restricted to AdEVs isolated from obese and insulin resistant, but not lean mice, which was consistent with differential protein loads and AdEV luminal morphologies. Likewise, in vivo pre-treatment with AdEVs from obese but not lean mice amplified insulin secretion and glucose tolerance in mice. This data suggests that secreted AdEVs can inform pancreatic β-cells about insulin resistance in adipose tissue in order to amplify GSIS in times of increased insulin demand

Microglial phagolysosome dysfunction and altered neural communication amplify phenotypic severity in Prader-Willi Syndrome with larger deletion

Prader-Willi Syndrome (PWS) is a rare neurodevelopmental disorder of genetic etiology, characterized by paternal deletion of genes located at chromosome 15 in 70% of cases. Two distinct genetic subtypes of PWS deletions are characterized, where type I (PWS T1) carries four extra haploinsufficient genes compared to type II (PWS T2). PWS T1 individuals display more pronounced physiological and cognitive abnormalities than PWS T2, yet the exact neuropathological mechanisms behind these differences remain unclear. Our study employed postmortem hypothalamic tissues from PWS T1 and T2 individuals, conducting transcriptomic analyses and cell-specific protein profiling in white matter, neurons, and glial cells to unravel the cellular and molecular basis of phenotypic severity in PWS sub-genotypes. In PWS T1, key pathways for cell structure, integrity, and neuronal communication are notably diminished, while glymphatic system activity is heightened compared to PWS T2. The microglial defect in PWS T1 appears to stem from gene haploinsufficiency, as global and myeloid-specific Cyfip1 haploinsufficiency in murine models demonstrated. Our findings emphasize microglial phagolysosome dysfunction and altered neural communication as crucial contributors to the severity of PWS T1’s phenotype

Towards the identification of regulatory networks using statistical and information theoretical methods on the mammalian transcriptome

Our comprehension of the genetic machinery regulating the expression of thousands of different genes controlling cell differentiation or responding to various external signals is still highly incomplete. Furthermore, recently discovered regulatory mechanisms like those mediated by microRNAs expand our knowledge but also add an additional layer of complexity. Since all genes are primarily transcribed into RNA, the genetic activity of gene differential expression can be estimated by measuring the RNA expression. Several techniques to measure large scale gene expression on the basis of RNA have been developed. In this work, data generated with the microarray technology, one of the most commonly used methods, were analyzed towards extracting novel biological regulatory structures. In this work, several aspects on the analysis of these large gene expression data are discussed. Since this is nowadays a common task, a lot has been written about various methods in all its particulars, but often from a more technical or statistical point of view. However, the aim of a biologist planning and carrying out a microarray experiment lies on the acquisition of novel biological findings. In fact, there is still a gap between the experimentalists and the methods developing community. The experimentalists are often not too familiar with the latest fancy method based on modern statistics as it is used in e.g. information theory whereas the developing community normally does not deal extensively with current biological questions. Therefore, the author of this work tries to give an additional view on the field of microarray analysis and the applicability of diverse methods. Hence, the focus is to discuss commonly used methods towards their usage, the underlying biological assumptions and the possible interpretations, pros and cons. Furthermore, beyond ordinary differential gene expression analyses, this work also concentrates on an unbiased search for hidden information in gene expression patterns. In the first section of chapter 1, a general overview about the main biological principles is given. The term transcriptome and its composition of several RNA types will be introduced. Furthermore the mechanism controlling gene expression will be presented. The chapter further explains the basic principles of microarray technology and also discusses the advantages and limitations of this method. Finally, by means of two different biological models, commonly used and a few more specialized and less popular analysis methods will be presented. In doing so, less emphasis is given on a complete and detailed mathematical description, but more on a general applicability and the biological outcome of these tools. Chapter 2 extensively discusses the usage of a blind source separation technique, independent component analysis (ICA), on a two class microarray dataset. Monocytes extracted from human donors were differentiated into macrophages using M-CSF (Macrophage Colony-Stimulating Factor). By applying ICA to the data, so called \textit{expression modes} or \textit{sub-modes} could be extracted. According to referring biological annotations, these sub-modes were then combined to \textit{meta modes} and elaborately discussed. In this way, several known biological signalling pathways as well as regulatory mechanism involved in monocyte differentiation could be reconstructed. Furthermore, a novel biological finding, the remaining proliferative potential of macrophages could also be identified [Lutter et al., 2008]. In chapter 3, again ICA was used, but in this case applied to time-dependent microarray data, and results were compared to a very common analysis method, hierarchical clustering. Time-dependent data was derived from human monocytes infected with the intracellular pathogen F. tularensis. Using the clustering approach, groups of genes referring to distinct timepoints were identified, and a temporal behaviour of genetic immune response could be reconstructed. In parallel, ICA was used to decompose the data into expression modes (analogously to chapter 2). These modes were then mapped on the experimental time course. Compared to the clustering results, the ICA-based reconstructed immune response was more detailed and temporal activity of distinct genes could be resolved more precisely [Lutter et al., 2009]. In the following chapter 4, three different microarray datasets were used to confirm a suggested regulatory mechanism. The observation that about 50% of all microRNAs in humans and mice are intronic and therefore coupled with the expression of protein coding genes, so-called host genes, allowed for the use of established large-scale gene expression measurement techniques to approximate microRNA expression. Since a single microRNA can regulate up to dozens of other protein-coding genes, the hypothesis that this expressional linkage includes an additional functional component was investigated. Using the ordinary clustering algorithm `hierarchical clustering' and an approach based on gene annotations, this hypothesis could be basically confirmed

GeneSet2miRNA: finding the signature of

cooperative miRNA activities in the gene list