Innate immunity represents the first line of defense in animals. We report a genome-wide in vivo Drosophila RNA interference screen to uncover genes involved in susceptibility or resistance to intestinal infection with the bacterium Serratia marcescens. We employed first whole-organism gene suppression followed by tissue-specific silencing in gut epithelium or hemocytes to identify several hundred genes involved in intestinal anti-bacterial immunity. Among the pathways identified, we showed that the JAK-STAT signaling pathway controls host defense in the gut by regulating stem cell proliferation and thus epithelial cell homeostasis. Thus, we revealed multiple genes involved in anti-bacterial defense and the regulation of innate immunity.

Mek1 and Mek2 (also known as Map2k1 and Map2k2, respectively) are evolutionarily conserved, dual-specificity kinases that mediate Erk1 and Erk2 activation during adhesion and growth factor signaling. Here we describe a previously uncharacterized, unexpected role of Mek1 in downregulating Mek2-dependent Erk signaling. Mek1 mediates the regulation of Mek2 in the context of a previously undiscovered Mek1-Mek2 complex. The Mek heterodimer is negatively regulated by Erk-mediated phosphorylation of Mek1 on Thr292, a residue missing in Mek2. Disabling this Erk-proximal negative-feedback step stabilizes the phosphorylation of both Mek2 and Erk in cultured cells and in vivo in Mek1 knockout embryos and mice. Thus, in disagreement with the current perception of the pathway, the role of Mek1 and Mek2 in growth factor-induced Erk phosphorylation is not interchangeable. Our data establish Mek1 as the crucial modulator of Mek and Erk signaling and have potential implications for the role of Mek1 and Mek2 in tumorigenesis.

In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms.

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Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository.

The kingdom of fungi provides model organisms for biotechnology, cell biology, genetics, and life sciences in general. Only when their phylogenetic relationships are stably resolved, can individual results from fungal research be integrated into a holistic picture of biology. However, and despite recent progress, many deep relationships within the fungi remain unclear. Here, we present the first phylogenomic study of an entire eukaryotic kingdom that uses a consistency criterion to strengthen phylogenetic conclusions. We reason that branches (splits) recovered with independent data and different tree reconstruction methods are likely to reflect true evolutionary relationships. Two complementary phylogenomic data sets based on 99 fungal genomes and 109 fungal expressed sequence tag (EST) sets analyzed with four different tree reconstruction methods shed light from different angles on the fungal tree of life. Eleven additional data sets address specifically the phylogenetic position of Blastocladiomycota, Ustilaginomycotina, and Dothideomycetes, respectively. The combined evidence from the resulting trees supports the deep-level stability of the fungal groups toward a comprehensive natural system of the fungi. In addition, our analysis reveals methodologically interesting aspects. Enrichment for EST encoded data—a common practice in phylogenomic analyses—introduces a strong bias toward slowly evolving and functionally correlated genes. Consequently, the generalization of phylogenomic data sets as collections of randomly selected genes cannot be taken for granted. A thorough characterization of the data to assess possible influences on the tree reconstruction should therefore become a standard in phylogenomic analyses.

Highlights d PROTAC resistance via disruption of rather than adaptation to oncoprotein degradation d PROTACs using different E3s/CRLs: resistance via similar pathways but different genes d Result of using two PROTACs depends on E3, target, and sequential versus concurrent use d E3s essential for and highly expressed in tumor cells are useful for future PROTACs

In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.