Network component analysis: Reconstruction of regulatory signals in biological systems

Liao, James C.; Boscolo, Riccardo; Yang, Young-Lyeol; Tran, Linh M.; Sabatti, Chiara; Roychowdhury, Vwani P.

doi:10.1073/pnas.2136632100

Cited by 554 publications

(698 citation statements)

References 20 publications

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“…PPARs are transcription factors of the nuclear receptor superfamily, and regulate gene expression of various genes (including GK) in response to fatty acids [54,55]. Our previous study [2] showed, by using transcriptome profiling and network component analysis [56], that GK deletion affects the transcription factor activity of PPARα, SREBP1c, and SREBP2 in mouse liver. Further, a PathwayAssist analysis revealed that GK is closely linked to PPARα and SREBPs [2].…”

Section: Discussionmentioning

confidence: 99%

Global metabolic effects of glycerol kinase overexpression in rat hepatoma cells

Sriram

Rahib

et al. 2008

Molecular Genetics and Metabolism

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Glycerol kinase has several diverse activities in mammalian cells. Glycerol kinase deficiency is a complex, single-gene, inborn error of metabolism wherein no genotype-phenotype correlation has been established. Since glycerol kinase has been suggested to exhibit additional activities than glycerol phosphorylation, expression level perturbation in this enzyme may affect cellular physiology globally. To investigate this possibility, we conducted metabolic investigations of wild type and two glycerol kinase-overexpressing H4IIE rat hepatoma cell lines constructed in this study. The glycerol kinase-overexpressing cell lines exhibited a significantly higher consumption of carbon sources per cell, suggesting excess carbon expenditure. Furthermore, we quantified intracellular metabolic fluxes by employing stable isotope ( 13 C) labeling with a mathematically designed substrate mixture, gas chromatography-mass spectrometry, and comprehensive isotopomer balancing. This flux analysis revealed that the pentose phosphate pathway flux in the glycerol kinase-overexpressing cell lines was two-fold higher than that in the wild type, in addition to subtler flux changes in other pathways of carbohydrate metabolism. Furthermore, the activity and transcript level of the lipogenic enzyme glucose-6-phosphate dehydrogenase, the rate-limiting enzyme of the pentose phosphate pathway, were also about two-fold higher than that of the wild type; these data corroborate the flux analysis results. This study shows that glycerol kinase affects carbon metabolism globally, possibly through its additional functions, and highlights glycerol kinase's multifaceted role in cellular physiology.

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Section: Discussionmentioning

confidence: 99%

Global metabolic effects of glycerol kinase overexpression in rat hepatoma cells

Sriram

Rahib

et al. 2008

Molecular Genetics and Metabolism

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“…Network Component Analysis-NCA is an algorithm that deduces system structure from sparsely connected networks, such as transcriptional regulatory networks (27,28). Other bipartite network analysis methods have been applied to biological data, such as a probabilistic state-space model (29) and an integration of expression and TF binding data (30).…”

Section: Methodsmentioning

confidence: 99%

“…NCA is a mathematical approach that employs biologically relevant constraints to identify perturbed regulators in transcriptome data by accounting for regulator activity and separating the effects of multiple regulators (27,28). Many TFs have both an active and an inactive state, where only the active state is able to affect transcript abundance.…”

Section: Gsno and No Inhibit Growth Through Distinct Mechanisms-mentioning

confidence: 99%

Determination of the Escherichia coli S-Nitrosoglutathione Response Network Using Integrated Biochemical and Systems Analysis

Jarboe¹,

Hyduke²,

Tran³

et al. 2008

Journal of Biological Chemistry

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During infection or denitrification, bacteria encounter reactive nitrogen species. Although the molecular targets of and defensive response against nitric oxide (NO) in Escherichia coli are well studied, the response elements specific to S-nitrosothiols are less clear. Previously, we employed an integrated systems biology approach to unravel the E. coli NO-response network. Here we use a similar approach to confirm that S-nitrosoglutathione (GSNO) primarily impacts the metabolic and regulatory programs of E. coli in minimal medium by reaction with homocysteine and cysteine and subsequent disruption of the methionine biosynthesis pathway. Targeting of homocysteine and cysteine results in altered regulatory activity of MetJ, MetR, and CysB, activation of the stringent response and growth inhibition. Deletion of metJ or supplementation with methionine strongly attenuated the effect of GSNO on growth and gene expression. Furthermore, GSNO inhibited the ArcAB two-component system. Consistent with the underlying nitrosative and thiol-oxidative chemistry, growth inhibition and the majority of the regulatory perturbations were dependent upon GSNO internalization by the Dpp dipeptide transporter. Contrastingly, perturbation of NsrR appeared to be a result of the submicromolar levels of NO released from GSNO and did not require GSNO internalization.Escherichia coli is a normal inhabitant of the human digestive tract but is also a causative agent of disease, including cystitis, pyelonephritis, and O157:H7-mediated hemolytic uremic syndrome (1-3). During the course of infection, E. coli is exposed to reactive nitrogen species (RNS) 5 produced by the mammalian immune system. The most important RNS are, arguably, nitric oxide (NO) and nitrosothiols (RSNO), such as S-nitrosoglutathione (GSNO) (4 -6). Mapping of the regulatory networks that govern the response to these compounds is relevant to understanding infection and in the identification of potential drug targets.NO and RSNO exhibit distinct chemical reactivities. NO reacts directly with metal centers and free radicals or mediates indirect effects by formation of other RNS in conjunction with oxygen or superoxide (7). Although RSNO can release NO via homolytic cleavage or reaction with copper (I) ions (8 -10), its primary biochemical effect is direct reaction with thiol groups through transnitrosation and S-thiolation (11). Additionally, NO can freely diffuse across membranes, whereas, GSNO susceptibility has been shown to be dependent on the Dpp dipeptide ABC transporter in Salmonella (12-14). As the first step for studying RNS challenge in E. coli, previous investigations often did not distinguish between RSNO and NO-mediated effects (15, 16) and RSNOs were sometimes used as NO donors (17). Such studies set the foundation for further distinction between RSNO and NO. A recent comparison of the E. coli response to NO and GSNO during chemostat growth confirmed the expectation that NO and GSNO mediate distinct transcriptional perturbations (18).E. coli possesses a NO-specifi...

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“…Here, we report the use of network component analysis (NCA) recently developed in our group (1) to determine the dynamics of the activities of various TFs during a physiological process. This approach uses both DNA microarray data and partial information regarding the membership of regulons as defined by each TF in question.…”

mentioning

confidence: 99%

Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis

Kao

Yang

Boscolo

et al. 2003

Proc. Natl. Acad. Sci. U.S.A.

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134

145

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Cells adjust gene expression profiles in response to environmental and physiological changes through a series of signal transduction pathways. Upon activation or deactivation, the terminal regulators bind to or dissociate from DNA, respectively, and modulate transcriptional activities on particular promoters. Traditionally, individual reporter genes have been used to detect the activity of the transcription factors. This approach works well for simple, nonoverlapping transcription pathways. For complex transcriptional networks, more sophisticated tools are required to deconvolute the contribution of each regulator. Here, we demonstrate the utility of network component analysis in determining multiple transcription factor activities based on transcriptome profiles and available connectivity information regarding network connectivity. We used Escherichia coli carbon source transition from glucose to acetate as a model system. Key results from this analysis were either consistent with physiology or verified by using independent measurements. B acteria respond to environmental changes through a variety of sensor proteins, which eventually relay the signals to corresponding DNA binding proteins to modulate transcription. The DNA binding transcription regulators, or transcription factors (TFs), typically require posttranscriptional modification or ligand binding to assume an active conformation, which may bind to DNA and either positively or negatively regulate transcription. Here, the activity of a TF is defined as the concentration of its subpopulation capable of DNA binding. The collective activities of TFs can thus be regarded as the physiological state of the cell. Determining these TF activities (TFAs) allows better understanding of how cells respond to changes in the environment. Careful experimental studies in the past few decades have identified conditions that perturb each individual TF independent of others. Although such ideal conditions allowed useful characterization of molecular mechanisms, most environmental perturbations are complex and are likely to provoke multiple regulatory systems simultaneously. Without a proper method of decomposing the regulatory signals, it is difficult to investigate how microorganisms coordinate various regulatory pathways upon an environmental challenge.Here, we report the use of network component analysis (NCA) recently developed in our group (1) to determine the dynamics of the activities of various TFs during a physiological process. This approach uses both DNA microarray data and partial information regarding the membership of regulons as defined by each TF in question. It contrasts with other approaches, such as singular value decomposition (2) or independent component analysis (3), in that it does not depend on orthogonality and statistical independence. Rather, it uses biological information regarding regulatory network topology, even when the topology is incompletely defined. Furthermore, NCA differs from modelbased parameter estimation (4) because it allows deconv...

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Network component analysis: Reconstruction of regulatory signals in biological systems

Cited by 554 publications

References 20 publications

Global metabolic effects of glycerol kinase overexpression in rat hepatoma cells

Global metabolic effects of glycerol kinase overexpression in rat hepatoma cells

Determination of the Escherichia coli S-Nitrosoglutathione Response Network Using Integrated Biochemical and Systems Analysis

Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis

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