When More Is Less: Dual Phosphorylation Protects Signaling Off State against Overexpression.

Kinases in signaling pathways are commonly activated by multisite phosphorylation. For example, the mitogen-activated protein kinase Erk is activated by its kinase Mek by two consecutive phosphorylations within its activation loop. In this article, we use kinetic models to study how the activation of Erk is coupled to its abundance. Intuitively, Erk activity should rise with increasing amounts of Erk protein. However, a mathematical model shows that the signaling off state is robust to increasing amounts of Erk, and Erk activity may even decline with increasing amounts of Erk. This counterintuitive, bell-shaped response of Erk activity to increasing amounts of Erk arises from the competition of the unmodified and single phosphorylated form of Erk for access to its kinase Mek. This shows that phosphorylation cycles can contain an intrinsic robustness mechanism that protects signaling from aberrant activation e.g., by gene expression noise or kinase overexpression after gene duplication events in diseases like cancer.

Analysis of impedance-based cellular growth assays.

MOTIVATION: Impedance-based technologies are advancing methods for measuring proliferation of adherent cell cultures non-invasively and in real time. The analysis of the resulting data has so far been hampered by inappropriate computational methods and the lack of systematic data to evaluate the characteristics of the assay. RESULTS: We used a commercially available system for impedance-based growth measurement (xCELLigence) and compared the reported cell index with data from microscopy. We found that the measured signal correlates linearly with the cell number throughout the time of an experiment with sufficient accuracy in subconfluent cell cultures. The resulting growth curves for various colon cancer cells could be well described with the empirical Richards growth model, which allows for extracting quantitative parameters (such as characteristic cycle times). We found that frequently used readouts like the cell index at a specific time or the area under the growth curve cannot be used to faithfully characterize growth inhibition. We propose to calculate the average growth rate of selected time intervals to accurately estimate time-dependent IC50 values of drugs from growth curves. CONTACT: nils.bluethgen@charite.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computer-assisted curation of a human regulatory core network from the biological literature.

MOTIVATION: A highly interlinked network of transcription factors (TFs) orchestrates the context-dependent expression of human genes. ChIP-chip experiments that interrogate the binding of particular TFs to genomic regions are used to reconstruct gene regulatory networks at genome-scale, but are plagued by high false-positive rates. Meanwhile, a large body of knowledge on high-quality regulatory interactions remains largely unexplored, as it is available only in natural language descriptions scattered over millions of scientific publications. Such data are hard to extract and regulatory data currently contain together only 503 regulatory relations between human TFs. RESULTS: We developed a text-mining-assisted workflow to systematically extract knowledge about regulatory interactions between human TFs from the biological literature. We applied this workflow to the entire Medline, which helped us to identify more than 45 000 sentences potentially describing such relationships. We ranked these sentences by a machine-learning approach. The top-2500 sentences contained ∼900 sentences that encompass relations already known in databases. By manually curating the remaining 1625 top-ranking sentences, we obtained more than 300 validated regulatory relationships that were not present in a regulatory database before. Full-text curation allowed us to obtain detailed information on the strength of experimental evidences supporting a relationship. CONCLUSIONS: We were able to increase curated information about the human core transcriptional network by >60% compared with the current content of regulatory databases. We observed improved performance when using the network for disease gene prioritization compared with the state-of-the-art. AVAILABILITY AND IMPLEMENTATION: Web-service is freely accessible at http://fastforward.sys-bio.net/. CONTACT: leser@informatik.hu-berlin.de or nils.bluethgen@charite.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Network quantification of EGFR signaling unveils potential for targeted combination therapy.

The epidermal growth factor receptor (EGFR) signaling network is activated in most solid tumors, and small-molecule drugs targeting this network are increasingly available. However, often only specific combinations of inhibitors are effective. Therefore, the prediction of potent combinatorial treatments is a major challenge in targeted cancer therapy. In this study, we demonstrate how a model-based evaluation of signaling data can assist in finding the most suitable treatment combination. We generated a perturbation data set by monitoring the response of RAS/PI3K signaling to combined stimulations and inhibitions in a panel of colorectal cancer cell lines, which we analyzed using mathematical models. We detected that a negative feedback involving EGFR mediates strong cross talk from ERK to AKT. Consequently, when inhibiting MAPK, AKT activity is increased in an EGFR-dependent manner. Using the model, we predict that in contrast to single inhibition, combined inactivation of MEK and EGFR could inactivate both endpoints of RAS, ERK and AKT. We further could demonstrate that this combination blocked cell growth in BRAF- as well as KRAS-mutated tumor cells, which we confirmed using a xenograft model.

Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS.

RAS mutations are highly relevant for progression and therapy response of human tumours, but the genetic network that ultimately executes the oncogenic effects is poorly understood. Here, we used a reverse-engineering approach in an ovarian cancer model to reconstruct KRAS oncogene-dependent cytoplasmic and transcriptional networks from perturbation experiments based on gene silencing and pathway inhibitor treatments. We measured mRNA and protein levels in manipulated cells by microarray, RT-PCR and western blot analysis, respectively. The reconstructed model revealed complex interactions among the transcriptional and cytoplasmic components, some of which were confirmed by double pertubation experiments. Interestingly, the transcription factors decomposed into two hierarchically arranged groups. To validate the model predictions, we analysed growth parameters and transcriptional deregulation in the KRAS-transformed epithelial cells. As predicted by the model, we found two functional groups among the selected transcription factors. The experiments thus confirmed the predicted hierarchical transcription factor regulation and showed that the hierarchy manifests itself in downstream gene expression patterns and phenotype.

Strong negative feedback from Erk to Raf confers robustness to MAPK signalling.

Protein levels within signal transduction pathways vary strongly from cell to cell. Here, we analysed how signalling pathways can still process information quantitatively despite strong heterogeneity in protein levels. We systematically perturbed the protein levels of Erk, the terminal kinase in the MAPK signalling pathway in a panel of human cell lines. We found that the steady-state phosphorylation of Erk is very robust against perturbations of Erk protein level. Although a multitude of mechanisms exist that may provide robustness against fluctuating protein levels, we found that one single feedback from Erk to Raf-1 accounts for the observed robustness. Surprisingly, robustness is provided through a fast post-translational mechanism although variation of Erk levels occurs on a timescale of days.

Chi3l3 induces oligodendrogenesis in an experimental model of autoimmune neuroinflammation.

In demyelinating diseases including multiple sclerosis (MS), neural stem cells (NSCs) can replace damaged oligodendrocytes if the local microenvironment supports the required differentiation process. Although chitinase-like proteins (CLPs) form part of this microenvironment, their function in this differentiation process is unknown. Here, we demonstrate that murine Chitinase 3-like-3 (Chi3l3/Ym1), human Chi3L1 and Chit1 induce oligodendrogenesis. In mice, Chi3l3 is highly expressed in the subventricular zone, a stem cell niche of the adult brain, and in inflammatory brain lesions during experimental autoimmune encephalomyelitis (EAE). We find that silencing Chi3l3 increases severity of EAE. We present evidence that in NSCs Chi3l3 activates the epidermal growth factor receptor (EGFR), thereby inducing Pyk2-and Erk1/2- dependent expression of a pro-oligodendrogenic transcription factor signature. Our results implicate CLP-EGFR-Pyk2-MEK-ERK as a key intrinsic pathway controlling oligodendrogenesis.

Effects of RAF inhibitors on PI3K/AKT signalling depend on mutational status of the RAS/RAF signalling axis.

Targeted therapies within the RAS/RAF/MEK/ERK signalling axis become increasingly popular, yet cross-talk and feedbacks in the signalling network lead to unexpected effects. Here we look systematically into how inhibiting RAF and MEK with clinically relevant inhibitors result in changes in PI3K/AKT activation. We measure the signalling response using a bead-based ELISA, and use a panel of three cell lines, and isogenic cell lines that express mutant forms of the oncogenes KRAS and BRAF to interrogate the effects of the MEK and RAF inhibitors on signalling. We find that treatment with the RAF inhibitors have opposing effects on AKT phosphorylation depending on the mutational status of two important oncogenes, KRAS and BRAF. If these two genes are in wildtype configuration, RAF inhibitors reduce AKT phosphorylation. In contrast, if BRAF or KRAS are mutant, RAF inhibitors will leave AKT phosphorylation unaffected or lead to an increase of AKT phosphorylation. Down-regulation of phospho-AKT by RAF inhibitors also extends to downstream transcription factors, and correlates with apoptosis induction. Our results show that oncogenes rewire signalling such that targeted therapies can have opposing effects on parallel pathways, which depend on the mutational status of the cell.

How scaffolds shape MAPK signaling: what we know and opportunities for systems approaches.

Scaffolding proteins add a new layer of complexity to the dynamics of cell signaling. Above their basic function to bring several components of a signaling pathway together, recent experimental research has found that scaffolds influence signaling in a much more complex way: scaffolds can exert some catalytic function, influence signaling by allosteric mechanisms, are feedback-regulated, localize signaling activity to distinct regions of the cell or increase pathway fidelity. Here we review experimental and theoretical approaches that address the function of two MAPK scaffolds, Ste5, a scaffold of the yeast mating pathway and KSR1/2, a scaffold of the classical mammalian MAPK signaling pathway. For the yeast scaffold Ste5, detailed mechanistic models have been valuable for the understanding of its function. For scaffolds in mammalian signaling, however, models have been rather generic and sketchy. For example, these models predicted narrow optimal scaffold concentrations, but when revisiting these models by assuming typical concentrations, rather a range of scaffold levels optimally supports signaling. Thus, more realistic models are needed to understand the role of scaffolds in mammalian signal transduction, which opens a big opportunity for systems biology.

Slow deactivation of ribulose 1,5-bisphosphate carboxylase/oxygenase elucidated by mathematical models.

Ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is the key enzyme of the Calvin cycle, catalyzing the fixation of inorganic carbon dioxide to organic sugars. Unlike most enzymes, RuBisCO is extremely slow, substrate unspecific, and catalyzes undesired side-reactions, which are considered to be responsible for the slow deactivation observed in vitro, a phenomenon known as fallover. Despite the fact that amino acid sequences and the 3D structures of RuBisCO from a variety of species are known, the precise molecular mechanisms for the various side reactions are still unclear. In the present study, we investigate the kinetic properties of RuBisCO using mathematical models. Initially, we formulate a minimal model that quantitatively reflects the kinetic behavior of RuBisCOs from different organisms. By relating rate parameters for single molecular steps to experimentally determined K(m) and V(max) values, we can examine mechanistic differences among species. The minimal model further demonstrates that two inhibitor producing side reactions are sufficient to describe experimentally determined fallover kinetics. To explain the observed kinetics of the limited capacity of RuBisCO to accept xylulose 1,5-bisphosphate as substrate, the inclusion of other side reactions is necessary. Our model results suggest a yet undescribed alternative enolization mechanism that is supported by the molecular structure. Taken together, the presented models serve as a theoretical framework to explain a wide range of observed kinetic properties of RuBisCOs derived from a variety of species. Thus, we can support hypotheses about molecular mechanisms and can systematically compare enzymes from different origins.