Dataset Information

Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data.

ABSTRACT: Identification of dynamic signaling mechanisms on different cellular layers is now facilitated as the increased usage of various high-throughput techniques goes along with decreasing costs for individual experiments. A lot of these signaling mechanisms are known to be coordinated by their dynamics, turning time-course data sets into valuable information sources for inference of regulatory mechanisms. However, the combined analysis of parallel time-course measurements from different high-throughput platforms still constitutes a major challenge requiring sophisticated bioinformatic tools in order to ease biological interpretation. We developed a new pathway-based integration approach for the analysis of coupled omics time-series data, which we implemented in the R package pwOmics. Unlike many other approaches, our approach acknowledges the role of the different cellular layers of measurement and infers consensus profiles and time profile clusters for further biological interpretation. We investigated a time-course data set on epidermal growth factor stimulation of human mammary epithelial cells generated on the two layers of RNA and proteins. The data was analyzed using our new approach with a focus on feedback signaling and pathway crosstalk. We could confirm known regulatory patterns relevant in the physiological cellular response to epidermal growth factor stimulation as well as identify interesting new interactions in this signaling context, such as the regulatory influence of the connective tissue growth factor on transferrin receptor or the influence of growth arrest and DNA-damage-inducible alpha on the connective tissue growth factor. Thus, we show that integrated cross-platform analysis provides a deeper understanding of regulatory signaling mechanisms. Combined with time-course information it enables the characterization of dynamic signaling processes and leads to the identification of important regulatory interactions which might be dysregulated in disease with adverse effects.

SUBMITTER: Wachter A

PROVIDER: S-EPMC4703778 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data.

Wachter Astrid A Beißbarth Tim T

Frontiers in genetics 20160107

Identification of dynamic signaling mechanisms on different cellular layers is now facilitated as the increased usage of various high-throughput techniques goes along with decreasing costs for individual experiments. A lot of these signaling mechanisms are known to be coordinated by their dynamics, turning time-course data sets into valuable information sources for inference of regulatory mechanisms. However, the combined analysis of parallel time-course measurements from different high-throughp ...[more]

PMID: 26779252

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Project description:Cellular heterogeneity, which plays an essential role in biological phenomena, such as drug resistance and migration, is considered to arise from intrinsic (i.e., reaction kinetics) and extrinsic (i.e., protein variability) noise in the cell. However, the mechanistic effects of these types of noise to determine the heterogeneity of signal responses have not been elucidated. Here, we report that the output of epidermal growth factor (EGF) signaling activity is modulated by cellular noise, particularly by extrinsic noise of particular signaling components in the pathway. We developed a mathematical model of the EGF signaling pathway incorporating regulation between extracellular signal-regulated kinase (ERK) and nuclear pore complex (NPC), which is necessary for switch-like activation of the nuclear ERK response. As the threshold of switch-like behavior is more sensitive to perturbations than the graded response, the effect of biological noise is potentially critical for cell fate decision. Our simulation analysis indicated that extrinsic noise, but not intrinsic noise, contributes to cell-to-cell heterogeneity of nuclear ERK. In addition, we accurately estimated variations in abundance of the signal proteins between individual cells by direct comparison of experimental data with simulation results using Apparent Measurement Error (AME). AME was constant regardless of whether the protein levels varied in a correlated manner, while covariation among proteins influenced cell-to-cell heterogeneity of nuclear ERK, suppressing the variation. Simulations using the estimated protein abundances showed that each protein species has different effects on cell-to-cell variation in the nuclear ERK response. In particular, variability of EGF receptor, Ras, Raf, and MEK strongly influenced cellular heterogeneity, while others did not. Overall, our results indicated that cellular heterogeneity in response to EGF is strongly driven by extrinsic noise, and that such heterogeneity results from variability of particular protein species that function as sensitive nodes, which may contribute to the pathogenesis of human diseases.

Project description:To date, the massive quantity of data generated by high-throughput techniques has not yet met bioinformatics treatment required to make full use of it. This is partially due to a mismatch in experimental and analytical study design but primarily due to a lack of adequate analytical approaches. When integrating multiple data types e.g. transcriptomics and metabolomics, multidimensional statistical methods are currently the techniques of choice. Typical statistical approaches, such as canonical correlation analysis (CCA), that are applied to find associations between metabolites and genes are failing due to small numbers of observations (e.g. conditions, diet etc.) in comparison to data size (number of genes, metabolites). Modifications designed to cope with this issue are not ideal due to the need to add simulated data resulting in a lack of p-value computation or by pruning of variables hence losing potentially valid information. Instead, our approach makes use of verified or putative molecular interactions or functional association to guide analysis. The workflow includes dividing of data sets to reach the expected data structure, statistical analysis within groups and interpretation of results. By applying pathway and network analysis, data obtained by various platforms are grouped with moderate stringency to avoid functional bias. As a consequence CCA and other multivariate models can be applied to calculate robust statistics and provide easy to interpret associations between metabolites and genes to leverage understanding of metabolic response. Effective integration of lipidomics and transcriptomics is demonstrated on publically available murine nutrigenomics data sets. We are able to demonstrate that our approach improves detection of genes related to lipid metabolism, in comparison to applying statistics alone. This is measured by increased percentage of explained variance (95% vs. 75-80%) and by identifying new metabolite-gene associations related to lipid metabolism.

Project description:BackgroundNelson Bay orthoreovirus (NBV) is a fusogenic bat borne virus with an unknown zoonotic potential. Previous studies have shown that NBV can infect and replicate in a wide variety of cell types derived from their natural host (bat), as well as from human, mouse and monkey. Within permissive cells, NBV induced significant cytopathic effects characterised by cell-cell fusion and syncytia formation. To understand the molecular events that underpin NBV infection we examined the host transcriptome and proteome response of two cell types, derived from bat (PaKiT03) and mouse (L929), to characterise differential cellular susceptibility to NBV.ResultsDespite significant differences in NBV replication and cytopathic effects in the L929 and PaKiT03 cells, the host response was remarkably similar in these cells. At both the transcriptome and proteome level, the host response was dominated by IFN production and signalling pathways. The majority of proteins up-regulated in L929 and PaKiT03 cells were also up-regulated at the mRNA (gene) level, and included many important IFN stimulated genes. Further functional experimentation demonstrated that stimulating IFN signalling prior to infection, significantly reduced NBV replication in PaKiT03 cells. Moreover, inhibiting IFN signalling (through specific siRNAs) increased NBV replication in L929 cells. In line with the significant cytopathic effects seen in PaKiT03 cells, we also observed a down-regulation of genes involved in cell-cell junctions, which may be related to the fusogenic effects of NBV.ConclusionsThis study provides new multi-dimensional insights into the host response of mammalian cells to NBV infection. We show that IFN activity is capable of reducing NBV replication, although it is unlikely that this is solely responsible for the reduced replication of NBV in L929 cells. The molecular events that underpin the fusogenic cytopathic effects described here will prove valuable for identifying potential therapeutic targets against fusogenic orthoreovirus.

Dataset Information

Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data.

Publications

Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data.

Similar Datasets

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