Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks

Zhang, Xiaofei; Ou-Yang, Le; Dai, Dao‐Qing; Wu, Mengyun; Zhu, Yuan; Yan, Hong

doi:10.1186/s12859-016-1233-0

Cited by 14 publications

(11 citation statements)

References 86 publications

(135 reference statements)

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“…The aim is to identify the minimum number of inputs, termed ‘driver’ nodes, that can steer the system from any initial state to any final state in finite time. Past studies of network controllability have identified the driver proteins to be associated with human diseases like cancer 22–26 and other molecular interaction networks 27–30 .…”

Section: Introductionmentioning

confidence: 99%

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems

Ravindran

Nacher

Akutsu

et al. 2019

Sci Rep

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In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high ‘control centrality’ of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks.

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Section: Introductionmentioning

confidence: 99%

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems

Ravindran

Nacher

Akutsu

et al. 2019

Sci Rep

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“…The important role of MD-nodes in network control is recognized both in theory (Nacher and Akutsu, 2012) and in various real-world networks (Nacher and Akutsu, 2013, Wan et al., 2002, Wuchty, 2014). In particular, the concept of MDSet has recently been adopted to analyze various biological networks, and the results showed that MD-nodes not only occupy strategic locations to control the networks but are also associated with various biological functions (Nacher and Akutsu, 2013, Nacher and Akutsu, 2016, Sun, 2015, Wakai et al., 2017, Wuchty, 2014, Zhang et al., 2016). Thus, we postulated that the composition of MD-nodes in a network represents its hidden control architecture.…”

Section: Introductionmentioning

confidence: 99%

The Hidden Control Architecture of Complex Brain Networks

et al. 2019

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Summary The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to delineate a control architecture of a complex network that is compatible with the behavior of the network and applied the framework to structural brain networks and other complex networks. As a result, we revealed that the brain networks have a distributed and overlapping control architecture governed by a small number of control nodes, which may be responsible for the robust and efficient brain functions. Moreover, our artificial network evolution analysis showed that the distributed and overlapping control architecture of the brain network emerges when it evolves toward increasing both robustness and efficiency.

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“…While the SMGs in our PPINs tended to be high-degree nodes, only a small number of their interactions exhibited frequent disruptions (in agreement with previous reports 28 ), unlike the case for many other genes of a similar degree whose interactions were often perturbed. One possible explanation for this is that a majority of the randomly selected genes were house-keeping genes occupying more central positions in the PPINs 29 and thus highly prone to rewiring as detailed by Kim et al 30 . Also, this can mean that SMGs have subtle effects on the PPIN and affect the same interaction partner consistently across patients.…”

Section: Resultsmentioning

confidence: 99%

Edgetic perturbation signatures represent known and novel cancer biomarkers

Kataka

Zaucha

Frishman

et al. 2020

Sci Rep

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isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RnA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer subtype specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at edgeexplorer: "http://webclu.bio.wzw.tum.de/edgeexplorer") are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.Cancer involves the accumulation of somatic mutations 1 and epigenetic modifications 2 , which drive the cells into the malignant state. Recurrent mutations implicated in tumorigenesis affect highly connected proteins within the protein interaction network 3,4 and are enriched at the interaction interfaces 5,6 and phosphorylation sites 7 signifying their role in rewiring protein interactions 8 . For this reason, cancer has been described as the disease of the interactome 9 . Indeed, the network of protein-protein interactions (PPI) has repeatedly allowed for the extraction of molecular features predictive of various phenotypic traits relevant to cancer -the so-called disease biomarkers 10 . For example, Cui et al. have identified putative interaction-disrupting mutations occurring at the interfaces of protein complexes and demonstrated that their presence is prognostic of poor survival 11 . In another study, Li et al. 12 developed the "OncoPPI" network of protein-protein interactions (PPIN) relevant to lung cancer, identifying biomarkers that can inform therapeutic decisions according to the drug sensitivity in certain conditions 12 . Nevertheless, the physical disruption of interaction sites by somatic mutations is only one mode of perturbing the interactome. Another relevant cellular process is regulating the expression (and thereby the local molecular concentration) of the interacting proteins 13 ; this has been utilized in mining the network of protein-protein interactions to identify modules of differentially expressed genes serving as robust biomarkers indicative of breast cancer metastasis 14 or stratifying patients from several breast cancer subtypes 15 . Furthermore, the phenomenon of "isoform switching", i.e. altering t...

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Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks

Cited by 14 publications

References 86 publications

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems

The Hidden Control Architecture of Complex Brain Networks

Edgetic perturbation signatures represent known and novel cancer biomarkers

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