Dataset Information

The exploration of network motifs as potential drug targets from post-translational regulatory networks.

ABSTRACT: Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs.

SUBMITTER: Zhang XD

PROVIDER: S-EPMC4744934 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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The exploration of network motifs as potential drug targets from post-translational regulatory networks.

Zhang Xiao-Dong XD Song Jiangning J Bork Peer P Zhao Xing-Ming XM

Scientific reports 20160208

Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify so ...[more]

PMID: 26853265

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Project description:Many essential functions in biological systems, including cell cycle progression and circadian rhythm regulation, are governed by the periodic behaviors of specific molecules. These periodic behaviors arise from the precise arrangement of components in biomolecular networks that generate oscillatory output signals. The dynamic properties of individual components of these networks, such as maturation delays and degradation rates, often play a key role in determining the network's oscillatory behavior. In this study, we explored the post-translational modulation of network components as a means to generate genetic circuits with oscillatory behaviors and perturb the oscillation features. Specifically, we used the NanoDeg platform-A bifunctional molecule consisting of a target-specific nanobody and a degron tag-to control the degradation rates of the circuit's components and predicted the effect of NanoDeg-mediated post-translational depletion of a key circuit component on the behavior of a series of proto-oscillating network topologies. We modeled the behavior of two main classes of oscillators, namely relaxation oscillator topologies (the activator-repressor and the Goodwin oscillator) and ring oscillator topologies (repressilators). We identified two main mechanisms by which non-oscillating networks could be induced to oscillate through post-translational modulation of network components: an increase in the separation of timescales of network components and mitigation of the leaky expression of network components. These results are in agreement with previous findings describing the effect of timescale separation and mitigation of leaky expression on oscillatory behaviors. This work thus validates the use of tools to control protein degradation rates as a strategy to modulate existing oscillatory signals and construct oscillatory networks. In addition, this study provides the design rules to implement such an approach based on the control of protein degradation rates using the NanoDeg platform, which does not require genetic manipulation of the network components and can be adapted to virtually any cellular protein. This work also establishes a framework to explore the use of tools for post-translational perturbations of biomolecular networks and generates desired behaviors of the network output.

Project description:Triple negative breast cancer (TNBC) is high-risk due to its rapid drug resistance and recurrence, metastasis, and lack of targeted therapy. So far, no molecularly targeted therapeutic agents have been clinically approved for TNBC. It is imperative that we discover new targets for TNBC therapy.A large volume of cancer genomics data are emerging and advancing breast cancer research. We may integrate different types of TNBC genomic data to discover molecular targets for TNBC therapy.We used publicly available TNBC tumor tissue genomic data in the Cancer Genome Atlas database in this study.We integratively explored genomic profiles (gene expression, copy number, methylation, microRNA [miRNA], and gene mutation) in TNBC and identified hyperactivated genes that have higher expression, more copy numbers, lower methylation level, or are targets of miRNAs with lower expression in TNBC than in normal samples. We ranked the hyperactivated genes into different levels based on all the genomic evidence and performed functional analyses of the sets of genes identified. More importantly, we proposed potential molecular targets for TNBC therapy based on the hyperactivated genes.Some of the genes we identified such as FGFR2, MAPK13, TP53, SRC family, MUC family, and BCL2 family have been suggested to be potential targets for TNBC treatment. Others such as CSF1R, EPHB3, TRIB1, and LAD1 could be promising new targets for TNBC treatment. By utilizing this integrative analysis of genomic profiles for TNBC, we hypothesized that some of the targeted treatment strategies for TNBC currently in development are more likely to be promising, such as poly (ADP-ribose) polymerase inhibitors, while the others are more likely to be discouraging, such as angiogenesis inhibitors.The findings in this study need to be experimentally validated in the future.This is a systematic study that combined 5 different types of genomic data to molecularly characterize TNBC and identify potential targets for TNBC therapy. The integrative analysis of genomic profiles for TNBC could assist in identifying potential new therapeutic targets and predicting the effectiveness of a targeted treatment strategy for TNBC therapy.

Dataset Information

The exploration of network motifs as potential drug targets from post-translational regulatory networks.

Publications

The exploration of network motifs as potential drug targets from post-translational regulatory networks.

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