Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Protein Cysteines Map to Functional Networks According to Steady-state Level of Oxidation.

ABSTRACT: The cysteine (Cys) proteome serves critical roles in protein structure, function and regulation, and includes key targets in oxidative mechanisms of disease. Thioredoxins maintain Cys residues in thiol forms, and previous research shows that the redox potential of thioredoxin in mitochondria and nuclei is more reduced than cytoplasm, suggesting that proteins in these compartments may have different steady-state oxidation. This study measured fractional oxidation of 641 peptidyl Cys residues from 333 proteins in HT29 cells by mass spectrometry. Average oxidation of cytoplasmic, nuclear and mitochondrial proteins was similar (15.8, 15.5, 14%, respectively). Pathway analysis showed that more reduced cytoplasmic Cys were in proteins associated with the cytoskeleton, more reduced nuclear Cys with Ran signaling and RNA post-transcriptional modifcation, and more reduced mitochondrial Cys with energy metabolism, cell growth and cell proliferation. More oxidized cytoplasmic Cys included associations with PI3/Akt, Myc-mediated apoptosis and 14-3-3-mediated signaling. Weaker associations of oxidized nuclear and mitochondrial Cys occurred with granzyme B signaling and intermediary metabolism, respectively. Thus, steady-state peptidyl Cys oxidation is associated with functional pathways rather than simply with organellar distribution. This suggests that oxidative mechanisms of disease could target functional pathways or networks rather than individual proteins or subcellular compartments.

SUBMITTER: Go YM

PROVIDER: S-EPMC3352318 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Scaling behaviour in steady-state contracting actomyosin networks.

Project description:Contractile actomyosin network flows are crucial for many cellular processes including cell division and motility, morphogenesis and transport. How local remodeling of actin architecture tunes stress production and dissipation and regulates large-scale network flows remains poorly understood. Here, we generate contracting actomyosin networks with rapid turnover in vitro, by encapsulating cytoplasmic Xenopus egg extracts into cell-sized 'water-in-oil' droplets. Within minutes, the networks reach a dynamic steady-state with continuous inward flow. The networks exhibit homogeneous, density-independent contraction for a wide range of physiological conditions, implying that the myosin-generated stress driving contraction and the effective network viscosity have similar density dependence. We further find that the contraction rate is roughly proportional to the network turnover rate, but this relation breaks down in the presence of excessive crosslinking or branching. Our findings suggest that cells use diverse biochemical mechanisms to generate robust, yet tunable, actin flows by regulating two parameters: turnover rate and network geometry.

| S-EPMC6871652 | biostudies-literature

Statistical mechanics for metabolic networks during steady state growth.

Project description:Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells.

| S-EPMC6065372 | biostudies-literature

Implementation of cell-free biological networks at steady state.

Project description:Living cells maintain a steady state of biochemical reaction rates by exchanging energy and matter with the environment. These exchanges usually do not occur in in vitro systems, which consequently go to chemical equilibrium. This in turn has severely constrained the complexity of biological networks that can be implemented in vitro. We developed nanoliter-scale microfluidic reactors that exchange reagents at dilution rates matching those of dividing bacteria. In these reactors we achieved transcription and translation at steady state for 30 h and implemented diverse regulatory mechanisms on the transcriptional, translational, and posttranslational levels, including RNA polymerases, transcriptional repression, translational activation, and proteolysis. We constructed and implemented an in vitro genetic oscillator and mapped its phase diagram showing that steady-state conditions were necessary to produce oscillations. This reactor-based approach will allow testing of whether fundamental limits exist to in vitro network complexity.

| S-EPMC3791785 | biostudies-literature

Exhaustive identification of steady state cycles in large stoichiometric networks.

Project description:BACKGROUND: Identifying cyclic pathways in chemical reaction networks is important, because such cycles may indicate in silico violation of energy conservation, or the existence of feedback in vivo. Unfortunately, our ability to identify cycles in stoichiometric networks, such as signal transduction and genome-scale metabolic networks, has been hampered by the computational complexity of the methods currently used. RESULTS: We describe a new algorithm for the identification of cycles in stoichiometric networks, and we compare its performance to two others by exhaustively identifying the cycles contained in the genome-scale metabolic networks of H. pylori, M. barkeri, E. coli, and S. cerevisiae. Our algorithm can substantially decrease both the execution time and maximum memory usage in comparison to the two previous algorithms. CONCLUSION: The algorithm we describe improves our ability to study large, real-world, biochemical reaction networks, although additional methodological improvements are desirable.

| S-EPMC2478680 | biostudies-literature

Quantitative regulation of the dynamic steady state of actin networks.

Project description:Principles of regulation of actin network dimensions are fundamentally important for cell functions, yet remain unclear. Using both in vitro and in silico approaches, we studied the effect of key parameters, such as actin density, ADF/Cofilin concentration and network width on the network length. In the presence of ADF/Cofilin, networks reached equilibrium and became treadmilling. At the trailing edge, the network disintegrated into large fragments. A mathematical model predicts the network length as a function of width, actin and ADF/Cofilin concentrations. Local depletion of ADF/Cofilin by binding to actin is significant, leading to wider networks growing longer. A single rate of breaking network nodes, proportional to ADF/Cofilin density and inversely proportional to the square of the actin density, can account for the disassembly dynamics. Selective disassembly of heterogeneous networks by ADF/Cofilin controls steering during motility. Our results establish general principles on how the dynamic steady state of actin network emerges from biochemical and structural feedbacks.

| S-EPMC6417862 | biostudies-literature

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

Project description:BackgroundComparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison.ResultsIn this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment.ConclusionsThrough evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

| S-EPMC5073945 | biostudies-literature

Density Functional Theory for Steady-State Nonequilibrium Molecular Junctions.

Project description:We present a density functional theory (DFT) for steady-state nonequilibrium quantum systems such as molecular junctions under a finite bias. Based on the steady-state nonequilibrium statistics that maps nonequilibrium to an effective equilibrium, we show that ground-state DFT (GS-DFT) is not applicable in this case and two densities, the total electron density and the density of current-carrying electrons, are needed to uniquely determine the properties of the corresponding nonequilibrium system. A self-consistent mean-field approach based on two densities is then derived. The theory is implemented into SIESTA computational package and applied to study nonequilibrium electronic/transport properties of a realistic carbon-nanotube (CNT)/Benzene junction. Results obtained from our steady-state DFT (SS-DFT) are compared with those of conventional GS-DFT based transport calculations. We show that SS-DFT yields energetically more stable nonequilibrium steady state, predicts significantly lower electric current, and is able to produce correct electronic structures in local equilibrium under a limiting case.

| S-EPMC4608000 | biostudies-literature

Dominant negative autoregulation limits steady-state repression levels in gene networks.

Project description:Many transcription factors repress transcription of their own genes. Negative autoregulation has been shown to reduce cell-cell variation in regulatory protein levels and speed up the response time in gene networks. In this work we examined transcription regulation of the galS gene and the function of its product, the GalS protein. We observed a unique operator preference of the GalS protein characterized by dominant negative autoregulation. We show that this pattern of regulation limits the repression level of the target genes in steady states. We suggest that transcription factors with dominant negative autoregulation are designed for regulating gene expression during environmental transitions.

| S-EPMC2704710 | biostudies-literature

Analysis of RNA-protein networks with RNP-MaP defines functional hubs on RNA.

Project description:RNA-protein interaction networks govern many biological processes but are difficult to examine comprehensively. We devised ribonucleoprotein networks analyzed by mutational profiling (RNP-MaP), a live-cell chemical probing strategy that maps cooperative interactions among multiple proteins bound to single RNA molecules at nucleotide resolution. RNP-MaP uses a hetero-bifunctional crosslinker to freeze interacting proteins in place on RNA and then maps multiple bound proteins on single RNA strands by read-through reverse transcription and DNA sequencing. RNP-MaP revealed that RNase P and RMRP, two sequence-divergent but structurally related non-coding RNAs, share RNP networks and that network hubs define functional sites in these RNAs. RNP-MaP also identified protein interaction networks conserved between mouse and human XIST long non-coding RNAs and defined protein communities whose binding sites colocalize and form networks in functional regions of XIST. RNP-MaP enables discovery and efficient validation of functional protein interaction networks on long RNAs in living cells.

| S-EPMC7956044 | biostudies-literature

Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation.

Project description:In metabolic networks, metabolites are usually present in great excess over the enzymes that catalyze their interconversion, and describing the rates of these reactions by using the Michaelis-Menten rate law is perfectly valid. This rate law assumes that the concentration of enzyme-substrate complex (C) is much less than the free substrate concentration (S0). However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S0 is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S0. We extend this description, which Borghans et al. call the total quasi-steady state approximation, to networks of coupled enzymatic reactions. First, we analyze an isolated Goldbeter-Koshland switch when enzymes and substrates are present in comparable concentrations. Then, on the basis of a real example of the molecular network governing cell cycle progression, we couple two and three Goldbeter-Koshland switches together to study the effects of feedback in networks of protein kinases and phosphatases. Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks, because (1) it unveils the modular structure of the enzymatic reactions, (2) it suggests a simple algorithm to formulate correct kinetic equations, and (3) contrary to classical Michaelis-Menten kinetics, it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively.

| S-EPMC1828705 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data