An approach for determining and measuring network hierarchy applied to comparing the phosphorylome and the regulome

Cheng, Chao; Andrews, Erik H.; Yan, Koon-Kiu; Ung, Matthew; Wang, Daifeng; Gerstein, Mark

doi:10.1186/s13059-015-0624-2

Cited by 29 publications

(33 citation statements)

References 57 publications

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“…2b), a result that has been already observed by Cheng et al [6]. We further found that the bottom layer is enriched for KPs located in the mating projection tip (Fig.…”

Section: Resultssupporting

confidence: 89%

“…The VS algorithm is among the best network decomposition algorithms available. It was conceived and applied by Jothi et al to the transcription regulatory network of the budding yeast Saccharomyces cerevisiae to elucidate the network hierarchical structure [5, 6, 8–11, 22]. The VS algorithm sorts nodes into different levels so that nodes in upper levels control those in lower levels [8].…”

Section: Resultsmentioning

confidence: 99%

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Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast

Abd-Rabbo

Michnick

2017

BMC Syst Biol

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BackgroundKinases and phosphatases (KP) form complex self-regulating networks essential for cellular signal processing. In spite of having a wealth of data about interactions among KPs and their substrates, we have very limited models of the structures of the directed networks they form and consequently our ability to formulate hypotheses about how their structure determines the flow of information in these networks is restricted.ResultsWe assembled and studied the largest bona fide kinase-phosphatase network (KP-Net) known to date for the yeast Saccharomyces cerevisiae. Application of the vertex sort (VS) algorithm on the KP-Net allowed us to elucidate its hierarchical structure in which nodes are sorted into top, core and bottom layers, forming a bow tie structure with a strongly connected core layer. Surprisingly, phosphatases tend to sort into the top layer, implying they are less regulated by phosphorylation than kinases. Superposition of the widest range of KP biological properties over the KP-Net hierarchy shows that core layer KPs: (i), receive the largest number of inputs; (ii), form bottlenecks implicated in multiple pathways and in decision-making; (iii), and are among the most regulated KPs both temporally and spatially. Moreover, top layer KPs are more abundant and less noisy than those in the bottom layer. Finally, we showed that the VS algorithm depends on node degrees without biasing the biological results of the sorted network. The VS algorithm is available as an R package (https://cran.r-project.org/web/packages/VertexSort/index.html).ConclusionsThe KP-Net model we propose possesses a bow tie hierarchical structure in which the top layer appears to ensure highest fidelity and the core layer appears to mediate signal integration and cell state-dependent signal interpretation. Our model of the yeast KP-Net provides both functional insight into its organization as we understand today and a framework for future investigation of information processing in yeast and eukaryotes in general.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0418-0) contains supplementary material, which is available to authorized users.

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“…2b), a result that has been already observed by Cheng et al [6]. We further found that the bottom layer is enriched for KPs located in the mating projection tip (Fig.…”

Section: Resultssupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast

Abd-Rabbo

Michnick

2017

BMC Syst Biol

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“…According to the model, Lsr2 and DosR both interact with three sigma factors directly, whereas Rv0691c interacts with four sigma factors indirectly through their corresponding anti-sigma factors. To examine the potential of these three TFs of regulating the whole sigma factor network, we inferred the hierarchical organization of the sigma factor network using the hierarchy score maximization algorithm (Cheng et al, 2015). This algorithm shows how a given directed network is organized in multiple levels and elucidates the position of each component at the inferred levels.…”

Section: Hierarchical Organization For Sigma Factors -Transcription Fmentioning

confidence: 99%

Network analysis identifies regulators of lineage-specific phenotypes in Mycobacterium tuberculosis

Banaei‐Esfahani

Trauner

Borrell

et al. 2020

Preprint

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Highlights Proteomic and transcriptomic characterization of fully sequenced diverse L1 and L2 clinical isolates of Mtb. Post-transcriptional control mechanisms for regulatory and virulence genes are mitigated in Mtb L2. By applying a genome-scale transcriptional framework, DosR, Rv1985c, Lsr2 and Rv0691c are identified as master transcription factors responsible for differential target gene expression in L2 strains compared to L1. L1 and L2 DosR proteins respond differently to nitric oxide stress, thus determining a relevant phenotype. SummaryThe Mycobacterium tuberculosis (Mtb) complex comprises seven phylogenetically distinct human-adapted lineages exhibiting different geographical distribution and degrees of pathogenicity. Among these, Lineage 1 (L1) has been associated with low virulence whereas Lineage 2 (L2) has been linked to hyper-virulence, enhanced transmission and drug resistance.Here, we conducted multi-layer comparative analyses using whole genome sequencing data combined with quantitative transcriptomic and proteomic profiling of a set of L1 and L2 clinical strains, each grown under two different conditions in vitro. Our data revealed different degrees of correlation between transcript and protein abundances across clinical strains and functional gene categories, indicating variable levels of post-transcriptional regulation in the tested lineages. Contrasting genomic and gene expression data showed that the magnitude of the transcriptional and translational changes was proportional to the phylogenetic distance between strains, with one out of three single nucleotide polymorphisms leading to a transcriptional and/or translational change on average. We devised a new genome-scale transcriptional regulatory model and identified several master transcription factors, strongly linked to the sigma factor network, whose targets were differentially regulated between the two lineages. These differences resulted in a higher basal expression of DosR proteins and a stronger response to nitric oxide (NO) exposure in L2 compared to L1. These patterns are most likely responsible for the shorter NO-induced growth arrest in L2 observed. Given the limited genetic variation between strains, it appears that phenotypic differences in Mtb are substantially driven by differences in the regulation of biochemical networks through master transcriptional regulators.

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“…In this study, we used the method developed by Cheng et al 34 to quantify the degree of hierarchy for the biological networks. Since we have created directed biological networks of genes, we are interested in examining node levels and whether the networks harbour hierarchical structures.…”

Section: Hierarchical Network Analysis and Reachabilitymentioning

confidence: 99%

“…In hierarchical network analysis, each node is assigned to a different hierarchical level that can demonstrate its biological role and importance in influencing other nodes. We applied the HSM algorithm 34 to classify the network into top, middle and bottom hierarchical layers ( Figure 3A; Supporting Information Table S1). Then, enrichment of each subnetwork was analysed in each hierarchical layer.…”

Section: Network Hierarchy and Biological Componentsmentioning

confidence: 99%

Novel putative drugs and key initiating genes for neurodegenerative disease determined using network‐based genetic integrative analysis

Mortezaei

Cazier

Mehrabi

et al. 2018

J of Cellular Biochemistry

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Understanding the genetic causes of neurodegenerative disease (ND) can be useful for their prevention and treatment. Among the genetic variations responsible for ND, heritable germline variants have been discovered in genome‐wide association studies (GWAS), and nonheritable somatic mutations have been discovered in sequencing projects. Distinguishing the important initiating genes in ND and comparing the importance of heritable and nonheritable genetic variants for treating ND are important challenges. In this study, we analysed GWAS results, somatic mutations and drug targets of ND from large databanks by performing directed network‐based analysis considering a randomised network hypothesis testing procedure. A disease‐associated biological network was created in the context of the functional interactome, and the nonrandom topological characteristics of directed‐edge classes were interpreted. Hierarchical network analysis indicated that drug targets tend to lie upstream of somatic mutations and germline variants. Furthermore, using directed path length information and biological explanations, we provide information on the most important genes in these created node classes and their associated drugs. Finally, we identified nine germline variants overlapping with drug targets for ND, seven somatic mutations close to drug targets from the hierarchical network analysis and six crucial genes in controlling other genes from the network analysis. Based on these findings, some drugs have been proposed for treating ND via drug repurposing. Our results provide new insights into the therapeutic actionability of GWAS results and somatic mutations for ND. The interesting properties of each node class and the existing relationships between them can broaden our knowledge of ND.

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An approach for determining and measuring network hierarchy applied to comparing the phosphorylome and the regulome

Cited by 29 publications

References 57 publications

Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast

Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast

Network analysis identifies regulators of lineage-specific phenotypes in Mycobacterium tuberculosis

Novel putative drugs and key initiating genes for neurodegenerative disease determined using network‐based genetic integrative analysis

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