Dominik Lutter

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

BackgroundMicroRNA-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.ResultsUsing 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.ConclusionsThese 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.

MicroRNAs coordinately regulate protein complexes

BackgroundIn animals, microRNAs (miRNAs) regulate the protein synthesis of their target messenger RNAs (mRNAs) by either translational repression or deadenylation. miRNAs are frequently found to be co-expressed in different tissues and cell types, while some form polycistronic clusters on genomes. Interactions between targets of co-expressed miRNAs (including miRNA clusters) have not yet been systematically investigated.ResultsHere we integrated information from predicted and experimentally verified miRNA targets to characterize protein complex networks regulated by human miRNAs. We found striking evidence that individual miRNAs or co-expressed miRNAs frequently target several components of protein complexes. We experimentally verified that the miR-141-200c cluster targets different components of the CtBP/ZEB complex, suggesting a potential orchestrated regulation in epithelial to mesenchymal transition.ConclusionsOur findings indicate a coordinate posttranscriptional regulation of protein complexes by miRNAs. These provide a sound basis for designing experiments to study miRNA function at a systems level.

The 2010 signal separation evaluation campaign (SiSEC2010): audio source separation

This paper introduces the audio part of the 2010 community-based Signal Separation Evaluation Campaign (SiSEC2010). Seven speech and music datasets were contributed, which include datasets recorded in noisy or dynamic environments, in addition to the SiSEC2008 datasets. The source separation problems were split into five tasks, and the results for each task were evaluated using different objective performance criteria. We provide an overview of the audio datasets, tasks and criteria. We also report the results achieved with the submitted systems, and discuss organization strategies for future campaigns.

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

BackgroundThe 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.ResultsStatistically 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 sub-mode. These sub-modes were further analyzed with different database query and literature mining tools and then combined to form so called meta-modes. With them we performed a knowledge-based pathway analysis and reconstructed a well known signal cascade.ConclusionWe 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.

Sharpening of expression domains induced by transcription and microRNA regulation within a spatio-temporal model of mid-hindbrain boundary formation

BackgroundThe establishment of the mid-hindbrain region in vertebrates is mediated by theisthmic organizer, an embryonic secondary organizer characterized by awell-defined pattern of locally restricted gene expression domains with sharplydelimited boundaries. While the function of the isthmic organizer at themid-hindbrain boundary has been subject to extensive experimental studies, itremains unclear how this well-defined spatial gene expression pattern, which isessential for proper isthmic organizer function, is established during vertebratedevelopment. Because the secreted Wnt1 protein plays a prominent role in isthmicorganizer function, we focused in particular on the refinement of Wnt1gene expression in this context.ResultsWe analyzed the dynamics of the corresponding murine gene regulatory network andthe related, diffusive signaling proteins using a macroscopic model for thebiological two-scale signaling process. Despite the discontinuity arisingfrom the sharp gene expression domain boundaries, we proved the existence ofunique, positive solutions for the partial differential equation system. Thisenabled the numerically and analytically analysis of the formation and stabilityof the expression pattern. Notably, the calculated expression domain ofWnt1 has no sharp boundary in contrast to experimental evidence. Wesubsequently propose a post-transcriptional regulatory mechanism for Wnt1miRNAs which yields the observed sharp expression domain boundaries. Weestablished a list of candidate miRNAs and confirmed their expression pattern byradioactive in situ hybridization. The miRNA miR-709 was identified as apotential regulator of Wnt1 mRNA, which was validated by luciferasesensor assays.ConclusionIn summary, our theoretical analysis of the gene expression pattern induction atthe mid-hindbrain boundary revealed the need to extend the model by an additionalWnt1 regulation. The developed macroscopic model of a two-scaleprocess facilitate the stringent analysis of other morphogen-based patterningprocesses.

miR-335 promotes mesendodermal lineage segregation and shapes a transcription factor gradient in the endoderm

Transcription factors (TFs) pattern developing tissues and determine cell fates; however, how spatio-temporal TF gradients are generated is ill defined. Here we show that miR-335 fine-tunes TF gradients in the endoderm and promotes mesendodermal lineage segregation. Initially, we identified miR-335 as a regulated intronic miRNA in differentiating embryonic stem cells (ESCs). miR-335 is encoded in the mesoderm-specific transcript (Mest) and targets the 3′-UTRs of the endoderm-determining TFs Foxa2 and Sox17. Mest and miR-335 are co-expressed and highly accumulate in the mesoderm, but are transiently expressed in endoderm progenitors. Overexpression of miR-335 does not affect initial mesendoderm induction, but blocks Foxa2- and Sox17-mediated endoderm differentiation in ESCs and ESC-derived embryos. Conversely, inhibition of miR-335 activity leads to increased Foxa2 and Sox17 protein accumulation and endoderm formation. Mathematical modeling predicts that transient miR-335 expression in endoderm progenitors shapes a TF gradient in the endoderm, which we confirm by functional studies in vivo. Taken together, our results suggest that miR-335 targets endoderm TFs for spatio-temporal gradient formation in the endoderm and to stabilize lineage decisions during mesendoderm formation.

Analyzing time-dependent microarray data using independent component analysis derived expression modes from human macrophages infected with F. tularensis holartica

The analysis of large-scale gene expression profiles is still a demanding and extensive task. Modern machine learning and data mining techniques developed in linear algebra, like Independent Component Analysis (ICA), become increasingly popular as appropriate tools for analyzing microarray data. We applied ICA to analyze kinetic gene expression profiles of human monocyte derived macrophages (MDM) from three different donors infected with Francisella tularensis holartica and compared them to more classical methods like hierarchical clustering. Results were compared using a pathway analysis tool, based on the Gene Ontology and the MeSH database. We could show that both methods lead to time-dependent gene regulatory patterns which fit well to known TNFalpha induced immune responses. In comparison, the nonexclusive attribute of ICA results in a more detailed view and a higher resolution in time dependent behavior of the immune response genes. Additionally, we identified NFkappaB as one of the main regulatory genes during response to F. tularensis infection.

Sparse Nonnegative Matrix Factorization with Genetic Algorithms for Microarray Analysis

Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. Gene expression profiles naturally conform to assumptions about data formats raised by NMF. However, it is known not to lead to unique results concerning the component signals extracted. In this paper we consider an extension of the NMF algorithm which provides unique solutions whenever the underlying component signals are sufficiently sparse. A new sparseness measure is proposed most appropriate to suitably transformed gene expression profiles. The resulting fitness function is discontinuous and exhibits many local minima, hence we use a genetic algorithm for its optimization. The algorithm is applied to toy data to investigate its properties as well as to a microarray data set related to Pseudo-Xanthoma Elasticum (PXE).

Pitchfork and Gprasp2 Target Smoothened to the Primary Cilium for Hedgehog Pathway Activation

The seven-transmembrane receptor Smoothened (Smo) activates all Hedgehog (Hh) signaling by translocation into the primary cilia (PC), but how this is regulated is not well understood. Here we show that Pitchfork (Pifo) and the G protein-coupled receptor associated sorting protein 2 (Gprasp2) are essential components of an Hh induced ciliary targeting complex able to regulate Smo translocation to the PC. Depletion of Pifo or Gprasp2 leads to failure of Smo translocation to the PC and lack of Hh target gene activation. Together, our results identify a novel protein complex that is regulated by Hh signaling and required for Smo ciliary trafficking and Hh pathway activation.

An Ensemble Approach for Inferring Semi-quantitative Regulatory Dynamics for the Differentiation of Mouse Embryonic Stem Cells Using Prior Knowledge

The process of differentiation of embryonic stem cells (ESCs) is currently becoming the focus of many systems biologists not only due to mechanistic interest but also since it is expected to play an increasingly important role in regenerative medicine, in particular with the advert to induced pluripotent stem cells. These ESCs give rise to the formation of the three germ layers and therefore to the formation of all tissues and organs. Here, we present a computational method for inferring regulatory interactions between the genes involved in ESC differentiation based on time resolved microarray profiles. Fully quantitative methods are commonly unavailable on such large-scale data; on the other hand, purely qualitative methods may fail to capture some of the more detailed regulations. Our method combines the beneficial aspects of qualitative and quantitative (ODE-based) modeling approaches searching for quantitative interaction coefficients in a discrete and qualitative state space. We further optimize on an ensemble of networks to detect essential properties and compare networks with respect to robustness. Applied to a toy model our method is able to reconstruct the original network and outperforms an entire discrete boolean approach. In particular, we show that including prior knowledge leads to more accurate results. Applied to data from differentiating mouse ESCs reveals new regulatory interactions, in particular we confirm the activation of Foxh1 through Oct4, mediating Nodal signaling.