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Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.

ABSTRACT: Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively will ultimately shape how we conceptualize the function, evolution and engineering of signaling networks.

SUBMITTER: Suderman R

PROVIDER: S-EPMC3794900 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.

Suderman Ryan R Deeds Eric J EJ

PLoS computational biology 20131010 10

Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function r ...[more]

PMID: 24130475

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Project description:MotivationIntegrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems.ResultsWe propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.Availability and implementationCode and data are available at https://github.com/GauravPandeyLab/ensemble_integration.Supplementary informationSupplementary data are available at Bioinformatics Advances online.

Project description:Herein, ruthenium complexes containing heterocyclic thioamidates [Ru(mmi)(bipy)(dppb)]PF6 (1), [Ru(tzdt)(bipy)(dppb)]PF6 (2), [Ru(dmp)(bipy)(dppb)]PF6 (3) and [Ru(mpca)(bipy)(dppb)]PF6 (4) were investigated for their cellular and molecular effects in cancer cell lines. Complexes 1 and 2 were the most potent of the four compounds against a panel of different cancer cell lines in monolayer cultures and showed potent cytotoxicity in a 3D model of multicellular spheroids that formed from human hepatocellular carcinoma HepG2 cells. In addition, both complexes were able to bind to DNA in a calf thymus DNA model. Compared to the controls, a reduction in cell proliferation, phosphatidylserine externalization, internucleosomal DNA fragmentation, and the loss of the mitochondrial transmembrane potential were observed in HepG2 cells that were treated with these complexes. Additionally, coincubation with a pan-caspase inhibitor (Z-VAD(OMe)-FMK) reduced the levels of apoptosis that were induced by these compounds compared to those in the negative controls, indicating that cell death through apoptosis occurred via a caspase-dependent pathway. Moreover, these complexes also induced the phosphorylation of ERK1/2, and coincubation with an MEK inhibitor (U0126), which is known to inhibit the activation of ERK1/2, but not JNK/SAPK and p38 MAPK inhibitors, reduced the complexes-induced apoptosis compared to that in the negative controls, indicating that the induction of apoptotic cell death occurred through ERK1/2 signaling in HepG2 cells. On the other hand, no increase in oxidative stress was observed in HepG2 cells treated with the complexes, and the complexes-induced apoptosis was not reduced with coincubation with the antioxidant N-acetylcysteine or a p53 inhibitor compared to that in the negative controls, indicating that apoptosis occurred via oxidative stress- and p53-independent pathways. Finally, these complexes also reduced the growth of HepG2 cells that were engrafted in C.B-17 SCID mice compared to that in the negative controls. These results indicated that these complexes are novel anticancer drug candidates for liver cancer treatment.

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Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.

Publications

Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.

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