Emerging properties of signaling networks in cancer: a data-derived modeling approach

Mammalian signal transduction pathways are highly integrated within extended networks, with crosstalk emerging in space and time. This dynamic circuitry is dependent on changing activity states for proteins and organelles. Network structures govern specificity of cellular responses to external stimuli, including proliferation and cell death. Loss of regulation virtually underlies all disease. However, while the contributions of individual components to phenotype are mostly well understood, systematic elucidation for the emergence or loss of crosstalk and impact on phenotype remains a fundamental challenge in classical biology that can be investigated by systems biology. To that end, we established a mathematical modeling platform, at the interface between experimental and theoretical approaches, to integrate prior literature knowledge with high-content, heterogeneous datasets for the non-intuitive prediction of adaptive signaling events. In the first part of this work, we investigated high-content microscopy datasets of morphological, bio-energetic and functional features of mitochondria in response to pro- apoptotic treatment in MCF-7 breast cancer cells. Data pretreatment techniques were used to unify the heterogeneous datasets. Using fuzzy logic, we established a generalized data-driven modeling formalism to model signaling events solely based on measurements, capable of high simulation accuracy via non-discrete rule sets. Employing neural networks, a generalized fuzzy logic system, i.e. its rules and membership functions, could be parameterized for each potential signaling interaction. An exhaustive search approach identified models with least error, i.e. the most related signaling events, and predicted a hierarchy of apoptotic events, in which upon activation of pro-apoptotic Bax, mitochondrial fragmentation propagates apoptosis, which is consistent with reported literature. Hence, we established a predictive approach for investigation of protein and organelle interactions utilizing cell-to-cell heterogeneity, a critical source of biologically relevant information. In the second part of this work, we sought to identify network evolution in the topology of MAPK signaling in the A-375 melanoma cell line. To that end, the modeling method was extended to incorporate temporal and topological structure from phosphorylation profiles of key MAPK intermediates treated with different pharmacological inhibitors and acquired over 96 hours. To increase prediction power, a parameter reduction strategy was developed to identify and fix parameters with lowest contribution to model performance. Therefore, training datasets were bootstrapped and signatures of deviation in flexibility and accuracy were calculated. This novel strategy achieved an optimal set of free parameters. Finally, a reduced multi-treatment model encoding the behavior of the full MAPK dataset was systematically trained to a sequentially increasing subset of time points, enabling time-defined identification of discrepancies in reported vs. acquired network topology. To that end, an objective function for fuzzy logic model optimization was implemented, which accounted for time-defined model training. Analysis led to the identification of emerging discrepancies between model and data at specific time points, thus characterizing a potential network rearrangement upstream of MAPK kinase MEK1, consistent with studies reporting increased resistance to apoptosis exhibited by A-375 melanoma cell line. The approach presented here was successfully benchmarked against a recently published fuzzy-logic-based analysis of signal transduction

Design of Specific Mammalian Promoters by in silico Prediction

The purpose of this RFC is to provide a) a method for the design of rational synthetic promoter sequences based on a statistical analysis about the spatial preference of transcription factor binding sites in human promoter sequences and b) further introduce standards to provide compatibility with data formats introduced in this RFC. Description of promoters generated by this method can be found at http://2009.igem.org/Team:Heidelberg/HEARTBEAT_database

MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis

Dysregulation of signaling pathways in multiple sclerosis (MS) can be analyzed by phosphoproteomics in peripheral blood mononuclear cells (PBMCs). We performed in vitro kinetic assays on PBMCs in 195 MS patients and 60 matched controls and quantified the phosphorylation of 17 kinases using xMAP assays. Phosphoprotein levels were tested for association with genetic susceptibility by typing 112 single-nucleotide polymorphisms (SNPs) associated with MS susceptibility. We found increased phosphorylation of MP2K1 in MS patients relative to the controls. Moreover, we identified one SNP located in the PHDGH gene and another on IRF8 gene that were associated with MP2K1 phosphorylation levels, providing a first clue on how this MS risk gene may act. The analyses in patients treated with disease-modifying drugs identified the phosphorylation of each receptor’s downstream kinases. Finally, using flow cytometry, we detected in MS patients increased STAT1, STAT3, TF65, and HSPB1 phosphorylation in CD19+ cells. These findings indicate the activation of cell survival and proliferation (MAPK), and proinflammatory (STAT) pathways in the immune cells of MS patients, primarily in B cells. The changes in the activation of these kinases suggest that these pathways may represent therapeutic targets for modulation by kinase inhibitors

Multi-Parametric Analysis and Modeling of Relationships between Mitochondrial Morphology and Apoptosis

Mitochondria exist as a network of interconnected organelles undergoing constant fission and fusion. Current approaches to study mitochondrial morphology are limited by low data sampling coupled with manual identification and classification of complex morphological phenotypes. Here we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneous, quantified datasets and infer relations between mitochondrial morphology and apoptotic events. We initially performed high-content, multi-parametric measurements of mitochondrial morphological, apoptotic, and energetic states by high-resolution imaging of human breast carcinoma MCF-7 cells. Subsequently, decision tree-based analysis was used to automatically classify networked, fragmented, and swollen mitochondrial subpopulations, at the single-cell level and within cell populations. Our results revealed subtle but significant differences in morphology class distributions in response to various apoptotic stimuli. Furthermore, key mitochondrial functional parameters including mitochondrial membrane potential and Bax activation, were measured under matched conditions. Data-driven fuzzy logic modeling was used to explore the non-linear relationships between mitochondrial morphology and apoptotic signaling, combining morphological and functional data as a single model. Modeling results are in accordance with previous studies, where Bax regulates mitochondrial fragmentation, and mitochondrial morphology influences mitochondrial membrane potential. In summary, we established and validated a platform for mitochondrial morphological and functional analysis that can be readily extended with additional datasets. We further discuss the benefits of a flexible systematic approach for elucidating specific and general relationships between mitochondrial morphology and apoptosis

An artificial miRNA system reveals that relative contribution of translational inhibition to miRNA-mediated regulation depends on environmental and developmental factors in <i>Arabidopsis thaliana</i>

<div><p>Development and fitness of any organism rely on properly controlled gene expression. This is especially true for plants, as their development is determined by both internal and external cues. MicroRNAs (miRNAs) are embedded in the genetic cascades that integrate and translate those cues into developmental programs. miRNAs negatively regulate their target genes mainly post-transcriptionally through two co-existing mechanisms; mRNA cleavage and translational inhibition. Despite our increasing knowledge about the genetic and biochemical processes involved in those concurrent mechanisms, little is known about their relative contributions to the overall miRNA-mediated regulation. Here we show that co-existence of cleavage and translational inhibition is dependent on growth temperature and developmental stage. We found that efficiency of an artificial miRNA-mediated (amiRNA) gene silencing declines with age during vegetative development in a temperature-dependent manner. That decline is mainly due to a reduction on the contribution from translational inhibition. Both, temperature and developmental stage were also found to affect mature amiRNA accumulation and the expression patterns of the core players involved in miRNA biogenesis and action. Therefore, that suggests that each miRNA family specifically regulates their respective targets, while temperature and growth might influence the performance of miRNA-dependent regulation in a more general way.</p></div

Data-derived modeling characterizes plasticity of MAPK signaling in melanoma.

The majority of melanomas have been shown to harbor somatic mutations in the RAS-RAF-MEK-MAPK and PI3K-AKT pathways, which play a major role in regulation of proliferation and survival. The prevalence of these mutations makes these kinase signal transduction pathways an attractive target for cancer therapy. However, tumors have generally shown adaptive resistance to treatment. This adaptation is achieved in melanoma through its ability to undergo neovascularization, migration and rearrangement of signaling pathways. To understand the dynamic, nonlinear behavior of signaling pathways in cancer, several computational modeling approaches have been suggested. Most of those models require that the pathway topology remains constant over the entire observation period. However, changes in topology might underlie adaptive behavior to drug treatment. To study signaling rearrangements, here we present a new approach based on Fuzzy Logic (FL) that predicts changes in network architecture over time. This adaptive modeling approach was used to investigate pathway dynamics in a newly acquired experimental dataset describing total and phosphorylated protein signaling over four days in A375 melanoma cell line exposed to different kinase inhibitors. First, a generalized strategy was established to implement a parameter-reduced FL model encoding non-linear activity of a signaling network in response to perturbation. Next, a literature-based topology was generated and parameters of the FL model were derived from the full experimental dataset. Subsequently, the temporal evolution of model performance was evaluated by leaving time-defined data points out of training. Emerging discrepancies between model predictions and experimental data at specific time points allowed the characterization of potential network rearrangement. We demonstrate that this adaptive FL modeling approach helps to enhance our mechanistic understanding of the molecular plasticity of melanoma

AmiR-LUC accumulation is developmentally and temperature-dependent.

<p><i>(A) Mature amiR-LUC accumulation assayed by qRT-PCR</i>. <i>Black dots represent one biological replicate each calculated from two technical replicates</i>. <i>Lines</i>, <i>(blue = 16</i>°<i>C</i>, <i>green = 23</i>°<i>C) represent the average between two biological replicates</i>. <i>“Inflores” stands for inflorescences</i>. <i>(B) Representative sRNA blot for amiR-LUC accumulation</i>. * shows tissues in which temperature has a significant effect in a pairwise comparison (p<0.05). Letters and lines show significant differences between tissues in ANOVA-test after Tukey correction (adjusted p<0.05).</p

miRNA mode of action is developmentally and temperature-dependent.

<p><i>(A) LUC mRNA expression levels assayed by qRT-PCR normalized to LUC mRNA in rLUC control plants (red dotted line)</i>. <i>Lines</i>, <i>(blue = 16</i>°<i>C</i>, <i>green = 23</i>°<i>C) represent the average between two biological replicates</i>. <i>(B) LUC protein levels</i>. <i>Black dots represent one biological replicate each calculated from two technical replicates</i>. <i>Lines</i>, <i>(blue = 16</i>°<i>C</i>, <i>green = 23</i>°<i>C) represent the average between two biological replicates normalized to LUC protein levels in rLUC control plants (red dotted line)</i>. <i>(C) Coexistence index is the ratio of average protein levels by average mRNA levels from each sample and condition</i>. <i>(D) AGO1 expression levels assayed by qRT-PCR</i>. <i>Black dots represent one biological replicate each calculated from two technical replicates</i>. <i>Lines</i>, <i>(blue = 16</i>°<i>C</i>, <i>green = 23</i>°<i>C) represent the average between two biological replicates</i>. <i>(E) AGO10 expression levels assayed by qRT-PCR</i>. <i>Black dots represent one biological replicate each calculated from two technical replicates</i>. <i>Lines</i>, <i>(blue = 16</i>°<i>C</i>, <i>green = 23</i>°<i>C) represent the average between two biological replicates</i>. <i>(A-E) “Inflores” stands for inflorescences</i>. * shows tissues in which temperature has a significant effect in a pairwise comparison (p<0.05). Letters and lines show significant differences between tissues in ANOVA-test after Tukey correction (adjusted p<0.05).</p

Effect of development and temperature on the expression of miRNA biogenesis factors.

<p><i>(A) DCL1</i>. <i>(B) HYL1</i>. <i>(C) DRB2</i>. <i>(D) SE</i>. <i>(E) CPL1</i>. <i>Black dots represent one biological replicate each calculated from two technical replicates</i>. <i>Lines</i>, <i>(blue = 16</i>°<i>C</i>, <i>green = 23</i>°<i>C) represent the average between two biological replicates</i>. <i>“Inflores” stands for inflorescences</i>. * shows tissues in which temperature has a significant effect in a pairwise comparison (p<0.05). Letters and lines show significant differences between tissues in ANOVA-test after Tukey correction (adjusted p<0.05).</p

Addressing developmental and environmental influence on miRNA-mediated regulation via a luciferase reporter system.

<p>(A) Discrete time points for tissue collection over Arabidopsis life cycle at 16°C (blue) and 23°C (green). (B) Representative pictures of the different leaf stages collected spanning vegetative development. Arrows point to the collected leaves. (C) Mature miR156 qRT-PCR to ensure that samples from both datasets (16°C and 23°C) were at equivalent developmental points. Black dots represent one biological replicate each, calculated from two technical replicates. Lines, (blue = 16°C, green = 23°C) represent the average between two biological replicates. “Inflores” stands for inflorescences.</p