Deciphering transcriptional regulation in cancer cells and development of a new method to identify key transcriptional regulators and their target genes

Cancer cells accumulate genetic changes during carcinogenesis. The dimension of these changes range from point mutations to large chromosomal aberrations. It has been widely accepted that essential genetic programs are thereby dysregulated that normally would prevent uncontrolled cellular division and growth. Transcription factors (TFs) are key proteins of gene regulation and are frequently associated with genetic pathologies, e.g. MYCN in neuroblastomas (NBs). Research on gene regulation -in general or condition-specific- thus is a central aspect in cancer research, and it is also the focus of my work. In a carcinogenesis model of NBs without MYCN-amplification, mutations of chromosome 11q (11q-CNA) are suspected to critically influence tumor development. We were able to refine this model by means of gene expression analysis on 11q-CNA in NBs with different clinical outcome. Gene expression profiles of NBs with unfavorable progression differed significantly between tumors with and without 11q-CNA, whereas 11q-CNA in NBs with favorable outcome is apparently compensated by a yet unknown mechanism. The TF-encoding gene CAMTA1 is located on the chromosomal region 1p, which is frequently deleted in NBs. In vitro experiments with ectopic induction of CAMTA1 yielded CAMTA1-regulated genes with different gene expression profiles that were functionally associated by enrichment analyses with cell cycle regulation and neuronal differentiation. The suggested role of CAMTA1 as a tumor suppressor gene was confirmed by additional in vivo experiments. Furthermore, we studied the effect of MYC and MYCN in NBs without MYCN-amplification and found that these TF also strongly regulate a large number of common target genes according to their own gene expression in these tumors. Promoter analyses and chromatin immunoprecipitation additionally supported the regulation of the determined target genes by MYC/MYCN. The genome-wide application of promoter and enrichment analyses on gene expression data from mouse models enabled us to predict target TFs of Rage signaling. E2f1 and E2f4 were validated experimentally as components of the Rage-dependent gene regulatory network. Finally, we used our experience from gene expression analysis to develop a novel machine learning method to precisely predict TF target gene relationships in human. We combined results from a genome-wide correlation meta-analysis on 4064 microarray gene expression profiles and promoter analyses on TF binding sites with known regulatory interactions between TFs and target genes in our approach. Our method outperformed other comparable methods in human, as we improved shortcomings of other algorithms specifically for higher eukaryotes, in particular the frequently (erroneously) assumed correlation between the mRNA expression of TFs and their target genes. We made our method freely available as a software package with multiple applications like the identification of key TFs in a multiplicity of cellular systems (e.g. cancer cells)

Erstnachweise von Paratrachelas maculatus in Österreich und Deutschland (Araneae, Corinnidae)

Three adult females of Paratrachelas maculatus (Thorell, 1875) were found inside a house in the south of Vienna, in a cellar in Cologne and in a house in Rüsselsheim. Additional notes on diet in captivity are presented

Towards a Verified Enumeration of All Tame Plane Graphs

In his proof of the Kepler conjecture, Thomas Hales introduced the notion of tame graphs and provided a Java program for enumerating all tame plane graphs. We have translated his Java program into an executable function in HOL ("the generator"), have formalized the notions of tameness and planarity in HOL, and have partially proved that the generator returns all tame plane graphs. Running the generator in ML has shows that the list of plane tame graphs ("the archive") that Thomas Hales also provides is complete. Once we have finished the completeness proof for the generator. In addition we checked the redundancy of the archive by formalising an executable notion of isomorphism between plane graphs, and checking if the archive contains only graphs produced by the generator. It turned out that 2257 of the 5128 graphs in the archive are either not tame or isomorphic to another graph in the archive

What, if anything, is Lycosa accentuata Latreille, 1817? – Review of a nomenclatural conundrum (Araneae: Lycosidae)

FIG. 2. — Neotype of Lycosa accentuata Latreille, 1817 (junior subjective synonym of Araneus trabalis Clerck, 1757) from forêt de Fontainebleau near Paris: A, dorsal view; B, ventral view; C, epigyne in situ. Scale bars: A, B, 5 mm; C, 0.2 mm.Published as part of Breitling, Rainer & Bauer, Tobias, 2022, What, if anything, is Lycosa accentuata Latreille, 1817? - Review of a nomenclatural conundrum (Araneae: Lycosidae), pp. 197-207 in Zoosystema 44 (8) on page 204, DOI: 10.5252/zoosystema2022v44a8, http://zenodo.org/record/646768

Extended multirate infinitesimal step methods: Derivation of order conditions

Multirate methods are specially designed for problems with multiple time scales. The multirate infinitesimal step method (MIS) was developed as a generalization of the so called split-explicit Runge–Kutta methods, where the integration of the fast part is conducted analytically. The MIS method was originally evolved for applications related to numerical weather prediction, i.e. the integration of the compressible Euler equation. In this work, an extension to MIS methods will be presented where an arbitrary Runge–Kutta method (RK) is applied for the integration of the fast component. Furthermore, the order convergence from the original MIS method will be reinvestigated including the derivation of conditions up to order four. Additionally will be presented how well-known methods such as recursive flux splitting multirate method, (Schlegel et al., 2012) partitioned Runge–Kutta method, (Jackiewicz and Vermiglio, 2000) and generalized additive Runge–Kutta method, (Sandu and Günther, 2015) are related to or can be cast as an extended MIS method. An exemplary MIS method of order four with five stages will show that the convergence behaviour not only depends on the applied method for the integration of the fast component. The method will further indicate that the used fast time step plays a significant role. © 2019 The Author(s

CLEVER: Clique-Enumerating Variant Finder

Next-generation sequencing techniques have facilitated a large scale analysis of human genetic variation. Despite the advances in sequencing speeds, the computational discovery of structural variants is not yet standard. It is likely that many variants have remained undiscovered in most sequenced individuals. Here we present a novel internal segment size based approach, which organizes all, including also concordant reads into a read alignment graph where max-cliques represent maximal contradiction-free groups of alignments. A specifically engineered algorithm then enumerates all max-cliques and statistically evaluates them for their potential to reflect insertions or deletions (indels). For the first time in the literature, we compare a large range of state-of-the-art approaches using simulated Illumina reads from a fully annotated genome and present various relevant performance statistics. We achieve superior performance rates in particular on indels of sizes 20--100, which have been exposed as a current major challenge in the SV discovery literature and where prior insert size based approaches have limitations. In that size range, we outperform even split read aligners. We achieve good results also on real data where we make a substantial amount of correct predictions as the only tool, which complement the predictions of split-read aligners. CLEVER is open source (GPL) and available from http://clever-sv.googlecode.com.Comment: 30 pages, 8 figure

Eliciting pension beneficiaries’ sustainability preferences:Netspar Design Paper 207

Pension funds are under social and political pressure to make their investment policies more sustainable. Furthermore, European legislation will increasingly require pension funds to explicitly measure participants’ preferences for sustainability in their investment policies. However, do pension fund participants prefer sustainable investments and do they want a say in how the fund does that? We assessed this by conducting two field experiments in which a Dutch pension fund gave its participants a real voice in its policy to advance sustainable investment