Carsten Marr

Social Transfer of Pathogenic Fungus Promotes Active Immunisation in Ant Colonies

Social contact with fungus-exposed ants leads to pathogen transfer to healthy nest-mates, causing low-level infections. These micro-infections promote pathogen-specific immune gene expression and protective immunization of nest-mates.

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.

Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios

The mechanisms underlying haematopoietic lineage decisions remain disputed. Lineage-affiliated transcription factors with the capacity for lineage reprogramming, positive auto-regulation and mutual inhibition have been described as being expressed in uncommitted cell populations. This led to the assumption that lineage choice is cell-intrinsically initiated and determined by stochastic switches of randomly fluctuating cross-antagonistic transcription factors. However, this hypothesis was developed on the basis of RNA expression data from snapshot and/or population-averaged analyses. Alternative models of lineage choice therefore cannot be excluded. Here we use novel reporter mouse lines and live imaging for continuous single-cell long-term quantification of the transcription factors GATA1 and PU.1 (also known as SPI1). We analyse individual haematopoietic stem cells throughout differentiation into megakaryocytic–erythroid and granulocytic–monocytic lineages. The observed expression dynamics are incompatible with the assumption that stochastic switching between PU.1 and GATA1 precedes and initiates megakaryocytic–erythroid versus granulocytic–monocytic lineage decision-making. Rather, our findings suggest that these transcription factors are only executing and reinforcing lineage choice once made. These results challenge the current prevailing model of early myeloid lineage choice.

Hierarchical Differentiation of Myeloid Progenitors Is Encoded in the Transcription Factor Network

Hematopoiesis is an ideal model system for stem cell biology with advanced experimental access. A systems view on the interactions of core transcription factors is important for understanding differentiation mechanisms and dynamics. In this manuscript, we construct a Boolean network to model myeloid differentiation, specifically from common myeloid progenitors to megakaryocytes, erythrocytes, granulocytes and monocytes. By interpreting the hematopoietic literature and translating experimental evidence into Boolean rules, we implement binary dynamics on the resulting 11-factor regulatory network. Our network contains interesting functional modules and a concatenation of mutual antagonistic pairs. The state space of our model is a hierarchical, acyclic graph, typifying the principles of myeloid differentiation. We observe excellent agreement between the steady states of our model and microarray expression profiles of two different studies. Moreover, perturbations of the network topology correctly reproduce reported knockout phenotypes in silico. We predict previously uncharacterized regulatory interactions and alterations of the differentiation process, and line out reprogramming strategies.

An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy

BackgroundIn recent years, high-throughput microscopy has emerged as a powerful tool to analyze cellular dynamics in an unprecedentedly high resolved manner. The amount of data that is generated, for example in long-term time-lapse microscopy experiments, requires automated methods for processing and analysis. Available software frameworks are well suited for high-throughput processing of fluorescence images, but they often do not perform well on bright field image data that varies considerably between laboratories, setups, and even single experiments.ResultsIn this contribution, we present a fully automated image processing pipeline that is able to robustly segment and analyze cells with ellipsoid morphology from bright field microscopy in a high-throughput, yet time efficient manner. The pipeline comprises two steps: (i) Image acquisition is adjusted to obtain optimal bright field image quality for automatic processing. (ii) A concatenation of fast performing image processing algorithms robustly identifies single cells in each image. We applied the method to a time-lapse movie consisting of ∼315,000 images of differentiating hematopoietic stem cells over 6 days. We evaluated the accuracy of our method by comparing the number of identified cells with manual counts. Our method is able to segment images with varying cell density and different cell types without parameter adjustment and clearly outperforms a standard approach. By computing population doubling times, we were able to identify three growth phases in the stem cell population throughout the whole movie, and validated our result with cell cycle times from single cell tracking.ConclusionsOur method allows fully automated processing and analysis of high-throughput bright field microscopy data. The robustness of cell detection and fast computation time will support the analysis of high-content screening experiments, on-line analysis of time-lapse experiments as well as development of methods to automatically track single-cell genealogies.

Network plasticity of pluripotency transcription factors in embryonic stem cells

Transcription factor (TF) networks are thought to regulate embryonic stem cell (ESC) pluripotency. However, TF expression dynamics and regulatory mechanisms are poorly understood. We use reporter mouse ESC lines allowing non-invasive quantification of Nanog or Oct4 protein levels and continuous long-term single-cell tracking and quantification over many generations to reveal diverse TF protein expression dynamics. For cells with low Nanog expression, we identified two distinct colony types: one re-expressed Nanog in a mosaic pattern, and the other did not re-express Nanog over many generations. Although both expressed pluripotency markers, they exhibited differences in their TF protein correlation networks and differentiation propensities. Sister cell analysis revealed that differences in Nanog levels are not necessarily accompanied by differences in the expression of other pluripotency factors. Thus, regulatory interactions of pluripotency TFs are less stringently implemented in individual self-renewing ESCs than assumed at present.

miTALOS: Analyzing the tissue-specific regulation of signaling pathways by human and mouse microRNAs

MicroRNAs (miRNAs) are an important class of post-transcriptional regulators of gene expression that are involved in various cellular and phenotypic processes. A number of studies have shown that miRNA expression is induced by signaling pathways. Moreover, miRNAs emerge as regulators of signaling pathways. Here, we present the miTALOS web resource, which provides insight into miRNA-mediated regulation of signaling pathways. As a novel feature, miTALOS considers the tissue-specific expression signatures of miRNAs and target transcripts to improve the analysis of miRNA regulation in biological pathways. MiTALOS identifies potential pathway regulation by (i) an enrichment analysis of miRNA targets genes and (ii) by using a proximity score to evaluate the functional role of miRNAs in biological pathways by their network proximity. Moreover, miTALOS integrates five different miRNA target prediction tools and two different signaling pathway resources (KEGG and NCI). A graphical visualization of miRNA targets in both KEGG and NCI PID signaling pathways is provided to illustrate their respective pathway context. We perform a functional analysis on prostate cancer-related miRNAs and are able to infer a model of miRNA-mediated regulation on tumor proliferation, mobility and anti-apoptotic behavior. miTALOS provides novel features that accomplish a substantial support to systematically infer regulation of signaling pathways mediated by miRNAs. The web-server is freely accessible at http://hmgu.de/cmb/mitalos.

Software tools for single-cell tracking and quantification of cellular and molecular properties

Software tools for single-cell tracking and quantification of cellular and molecular properties

Topology regulates pattern formation capacity of binary cellular automata on graphs

We study the effect of topology variation on the dynamic behavior of a system with local update rules. We implement one-dimensional binary cellular automata on graphs with various topologies by formulating two sets of degree-dependent rules, each containing a single parameter. We observe that changes in graph topology induce transitions between different dynamic domains (Wolfram classes) without a formal change in the update rule. Along with topological variations, we study the pattern formation capacities of regular, random, small-world and scale-free graphs. Pattern formation capacity is quantified in terms of two entropy measures, which for standard cellular automata allow a qualitative distinction between the four Wolfram classes. A mean-field model explains the dynamic behavior of random graphs. Implications for our understanding of information transport through complex, network-based systems are discussed.

Similar impact of topological and dynamic noise on complex patterns

Shortcuts in a regular architecture affect the information transport through the system due to the severe decrease in average path length. A fundamental new perspective in terms of pattern formation is the destabilizing effect of topological perturbations by processing distant uncorrelated information, similarly to stochastic noise. We study the functional coincidence of rewiring and noisy communication on patterns of binary cellular automata.