An ultrasensitive sorting mechanism for EGF receptor endocytosis

Background The EGF receptor has been shown to internalize via clathrin-independent endocytosis (CIE) in a ligand concentration dependent manner. From a modeling point of view, this resembles an ultrasensitive response, which is the ability of signaling networks to suppress a response for low input values and to increase to a pre-defined level for inputs exceeding a certain threshold. Several mechanisms to generate this behaviour have been described theoretically, the underlying assumptions of which, however, have not been experimentally demonstrated for the EGF receptor internalization network. Results Here, we present a mathematical model of receptor sorting into alternative pathways that explains the EGF-concentration dependent response of CIE. The described mechanism involves a saturation effect of the dominant clathrin-dependent endocytosis pathway and implies distinct steady-states into which the system is forced for low vs high EGF stimulations. The model is minimal since no experimentally unjustified reactions or parameter assumptions are imposed. We demonstrate the robustness of the sorting effect for large parameter variations and give an analytic derivation for alternative steady-states that are reached. Further, we describe extensibility of the model to more than two pathways which might play a role in contexts other than receptor internalization. Conclusions Our main result is that a scenario where different endocytosis routes consume the same form of receptor corroborates the observation of a clear-cut, stimulus dependent sorting. This is especially important since a receptor modification discriminating between the pathways has not been found. The model is not restricted to EGF receptor internalization and might account for ultrasensitivity in other cellular contexts

Computational identification of signalling pathways in Plasmodium falciparum

Malaria is one of the world’s most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Reports have shown that the resistance of the parasite to existing drugs is increasing. Therefore, there is a huge and urgent need to discover and validate new drug or vaccine targets to enable the development of new treatments for malaria. The ability to discover these drug or vaccine targets can only be enhanced from our deep understanding of the detailed biology of the parasite, for example how cells function and how proteins organize into modules such as metabolic, regulatory and signal transduction pathways. It has been noted that the knowledge of signalling transduction pathways in Plasmodium is fundamental to aid the design of new strategies against malaria. This work uses a linear-time algorithm for finding paths in a network under modified biologically motivated constraints. We predicted several important signalling transduction pathways in Plasmodium falciparum. We have predicted a viable signalling pathway characterized in terms of the genes responsible that may be the PfPKB pathway recently elucidated in Plasmodium falciparum. We obtained from the FIKK family, a signal transduction pathway that ends up on a chloroquine resistance marker protein, which indicates that interference with FIKK proteins might reverse Plasmodium falciparum from resistant to sensitive phenotype. We also proposed a hypothesis that showed the FIKK proteins in this pathway as enabling the resistance parasite to have a mechanism for releasing chloroquine (via an efflux process). Furthermore, we also predicted a signalling pathway that may have been responsible for signalling the start of the invasion process of Red Blood Cell (RBC) by the merozoites. It has been noted that the understanding of this pathway will give insight into the parasite virulence and will facilitate rational vaccine design against merozoites invasion. And we have a host of other predicted pathways, some of which have been used in this work to predict the functionality of some proteins

Fresh Food Vending Trends and Practices

Fresh food vending represents $1.5 billion in sales each year in the United States. The implications for a better understanding of fresh food vending are significant in terms of profitability and improved market share for vending operators. Of equal importance is a better understanding of the significance of the route driver on the overall fresh food vending operation. Developing a better understanding of this area of the food service industry will help vending operators increase profits and provide better product choices to consumer

RA-PCR, a method for the generation of randomized promoter libraries

The purpose of this RFC is to provide instructions for the synthesis of promoters in mammalian cells that are active at a desired cellular condition (where a cellular condition is specified by the activity of a set of transcription factors of interest). The method generates a library of promoters putatively active under the desired condition. This RFC also provides instructions on how to screen the libraries generated by this method in order to obtain functional promoters. Description of promoters generated by this method can be found at http://2009.igem.org/Team:Heidelberg/Project_Synthetic_promoters

Decision-Tree Based Model Analysis for Efficient Identification of Parameter Relations Leading to Different Signaling States

In systems biology, a mathematical description of signal transduction processes is used to gain a more detailed mechanistic understanding of cellular signaling networks. Such models typically depend on a number of parameters that have different influence on the model behavior. Local sensitivity analysis is able to identify parameters that have the largest effect on signaling strength. Bifurcation analysis shows on which parameters a qualitative model response depends. Most methods for model analysis are intrinsically univariate. They typically cannot consider combinations of parameters since the search space for such analysis would be too large. This limitation is important since activation of a signaling pathway often relies on multiple rather than on single factors. Here, we present a novel method for model analysis that overcomes this limitation. As input to a model defined by a system of ordinary differential equations, we consider parameters for initial chemical species concentrations. The model is used to simulate the system response, which is then classified into pre-defined classes (e.g., active or not active). This is combined with a scan of the parameter space. Parameter sets leading to a certain system response are subjected to a decision tree algorithm, which learns conditions that lead to this response. We compare our method to two alternative multivariate approaches to model analysis: analytical solution for steady states combined with a parameter scan, and direct Lyapunov exponent (DLE) analysis. We use three previously published models including a model for EGF receptor internalization and two apoptosis models to demonstrate the power of our approach. Our method reproduces critical parameter relations previously obtained by both steady-state and DLE analysis while being more generally applicable and substantially less computationally expensive. The method can be used as a general tool to predict multivariate control strategies for pathway activation and to suggest strategies for drug intervention

The landscape of viral associations in human cancers

Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, for which whole-genome and—for a subset—whole-transcriptome sequencing data from 2,658 cancers across 38 tumor types was aggregated, we systematically investigated potential viral pathogens using a consensus approach that integrated three independent pipelines. Viruses were detected in 382 genome and 68 transcriptome datasets. We found a high prevalence of known tumor-associated viruses such as Epstein–Barr virus (EBV), hepatitis B virus (HBV) and human papilloma virus (HPV; for example, HPV16 or HPV18). The study revealed significant exclusivity of HPV and driver mutations in head-and-neck cancer and the association of HPV with APOBEC mutational signatures, which suggests that impaired antiviral defense is a driving force in cervical, bladder and head-and-neck carcinoma. For HBV, HPV16, HPV18 and adeno-associated virus-2 (AAV2), viral integration was associated with local variations in genomic copy numbers. Integrations at the TERT promoter were associated with high telomerase expression evidently activating this tumor-driving process. High levels of endogenous retrovirus (ERV1) expression were linked to a worse survival outcome in patients with kidney cancer

EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations

Background: High-throughput sequencing data are dramatically increasing in volume. Thus, there is urgent need for efficient tools to perform fast and integrative analysis of multiple data types. Enriched heatmap is a specific form of heatmap that visualizes how genomic signals are enriched over specific target regions. It is commonly used and efficient at revealing enrichment patterns especially for high dimensional genomic and epigenomic datasets. Results: We present a new R package named EnrichedHeatmap that efficiently visualizes genomic signal enrichment. It provides advanced solutions for normalizing genomic signals within target regions as well as offering highly customizable visualizations. The major advantage of EnrichedHeatmap is the ability to conveniently generate parallel heatmaps as well as complex annotations, which makes it easy to integrate and visualize comprehensive overviews of the patterns and associations within and between complex datasets. Conclusions: EnrichedHeatmap facilitates comprehensive understanding of high dimensional genomic and epigenomic data. The power of EnrichedHeatmap is demonstrated by visualization of the complex associations between DNA methylation, gene expression and various histone modifications

Death receptor-based enrichment of Cas9-expressing cells

Background: The CRISPR/Cas9 genome editing system has greatly facilitated and expanded our capacity to engineer mammalian genomes, including targeted gene knock-outs. However, the phenotyping of the knock-out effect requires a high DNA editing efficiency. Results: Here, we report a user-friendly strategy based on the extrinsic apoptosis pathway that allows enrichment of a polyclonal gene-edited cell population, by selecting Cas9-transfected cells that co-express dominant-negative mutants of death receptors. The extrinsic apoptosis pathway can be triggered in many mammalian cell types, and ligands are easy to produce, do not require purification and kill much faster than the state-of-the-art selection drug puromycin. Stringent assessment of our advanced selection strategy via Sanger sequencing, T7 endonuclease I (T7E1) assay and direct phenotyping confirmed a strong and rapid enrichment of Cas9-expressing cell populations, in some cases reaching up to 100 % within one hour. Notably, the efficiency of target DNA cleavage in these enriched cells reached high levels that exceeded the reliable range of the T7E1 assay, a conclusion that can be generalized for editing efficiencies above 30 %. Moreover, our data emphasize that the insertion and deletion pattern induced by a specific gRNA is reproducible across different cell lines. Conclusions: The workflow and the findings reported here should streamline a wide array of future low- or high-throughput gene knock-out screens, and should largely improve data interpretation from CRISPR experiments

Between-species differences in gene copy number are enriched among functions critical for adaptive evolution in Arabidopsis halleri

Complete dataset expanded from Additional file 3. (XLS 16282 kb