Analysis on relationship between extreme pathways and correlated reaction sets

Background: Constraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms. In constraint-based modeling, in order to characterize all allowable phenotypes, network-based pathways, such as extreme pathways and elementary flux modes, are defined. However, as the scale of metabolic network rises, the number of extreme pathways and elementary flux modes increases exponentially. Uniform random sampling solves this problem to some extent to study the contents of the available phenotypes. After uniform random sampling, correlated reaction sets can be identified by the dependencies between reactions derived from sample phenotypes. In this paper, we study the relationship between extreme pathways and correlated reaction sets.Results: Correlated reaction sets are identified for E. coli core, red blood cell and Saccharomyces cerevisiae metabolic networks respectively. All extreme pathways are enumerated for the former two metabolic networks. As for Saccharomyces cerevisiae metabolic network, because of the large scale, we get a set of extreme pathways by sampling the whole extreme pathway space. In most cases, an extreme pathway covers a correlated reaction set in an \u27all or none\u27 manner, which means either all reactions in a correlated reaction set or none is used by some extreme pathway. In rare cases, besides the \u27all or none\u27 manner, a correlated reaction set may be fully covered by combination of a few extreme pathways with related function, which may bring redundancy and flexibility to improve the survivability of a cell. In a word, extreme pathways show strong complementary relationship on usage of reactions in the same correlated reaction set.Conclusion: Both extreme pathways and correlated reaction sets are derived from the topology information of metabolic networks. The strong relationship between correlated reaction sets and extreme pathways suggests a possible mechanism: as a controllable unit, an extreme pathway is regulated by its corresponding correlated reaction sets, and a correlated reaction set is further regulated by the organism\u27s regulatory network.<br /

Matrix Formalism to Describe Functional States of Transcriptional Regulatory Systems

Complex regulatory networks control the transcription state of a genome. These transcriptional regulatory networks (TRNs) have been mathematically described using a Boolean formalism, in which the state of a gene is represented as either transcribed or not transcribed in response to regulatory signals. The Boolean formalism results in a series of regulatory rules for the individual genes of a TRN that in turn can be used to link environmental cues to the transcription state of a genome, thereby forming a complete transcriptional regulatory system (TRS). Herein, we develop a formalism that represents such a set of regulatory rules in a matrix form. Matrix formalism allows for the systemic characterization of the properties of a TRS and facilitates the computation of the transcriptional state of the genome under any given set of environmental conditions. Additionally, it provides a means to incorporate mechanistic detail of a TRS as it becomes available. In this study, the regulatory network matrix, R, for a prototypic TRS is characterized and the fundamental subspaces of this matrix are described. We illustrate how the matrix representation of a TRS coupled with its environment (R*) allows for a sampling of all possible expression states of a given network, and furthermore, how the fundamental subspaces of the matrix provide a way to study key TRS features and may assist in experimental design

Integration of microRNA changes in vivo identifies novel molecular features of muscle insulin resistance in type 2 diabetes

Skeletal muscle insulin resistance (IR) is considered a critical component of type II diabetes, yet to date IR has evaded characterization at the global gene expression level in humans. MicroRNAs (miRNAs) are considered fine-scale rheostats of protein-coding gene product abundance. The relative importance and mode of action of miRNAs in human complex diseases remains to be fully elucidated. We produce a global map of coding and non-coding RNAs in human muscle IR with the aim of identifying novel disease biomarkers. We profiled &gt;47,000 mRNA sequences and &gt;500 human miRNAs using gene-chips and 118 subjects (n = 71 patients versus n = 47 controls). A tissue-specific gene-ranking system was developed to stratify thousands of miRNA target-genes, removing false positives, yielding a weighted inhibitor score, which integrated the net impact of both up- and down-regulated miRNAs. Both informatic and protein detection validation was used to verify the predictions of in vivo changes. The muscle mRNA transcriptome is invariant with respect to insulin or glucose homeostasis. In contrast, a third of miRNAs detected in muscle were altered in disease (n = 62), many changing prior to the onset of clinical diabetes. The novel ranking metric identified six canonical pathways with proven links to metabolic disease while the control data demonstrated no enrichment. The Benjamini-Hochberg adjusted Gene Ontology profile of the highest ranked targets was metabolic (P &lt; 7.4 × 10-8), post-translational modification (P &lt; 9.7 × 10-5) and developmental (P &lt; 1.3 × 10-6) processes. Protein profiling of six development-related genes validated the predictions. Brain-derived neurotrophic factor protein was detectable only in muscle satellite cells and was increased in diabetes patients compared with controls, consistent with the observation that global miRNA changes were opposite from those found during myogenic differentiation. We provide evidence that IR in humans may be related to coordinated changes in multiple microRNAs, which act to target relevant signaling pathways. It would appear that miRNAs can produce marked changes in target protein abundance in vivo by working in a combinatorial manner. Thus, miRNA detection represents a new molecular biomarker strategy for insulin resistance, where micrograms of patient material is needed to monitor efficacy during drug or life-style interventions

Determination of an Interaction Network between an Extracellular Bacterial Pathogen and the Human Host

A major gap in understanding infectious diseases is the lack of information about molecular interaction networks between pathogens and the human host. Haemophilus ducreyi causes the genital ulcer disease chancroid in adults and is a leading cause of cutaneous ulcers in children in the tropics. We developed a model in which human volunteers are infected on the upper arm with H. ducreyi until they develop pustules. To define the H. ducreyi and human interactome, we determined bacterial and host transcriptomic and host metabolomic changes in pustules. We found that in vivo H. ducreyi transcripts were distinct from those in the inocula, as were host transcripts in pustule and wounded control sites. Many of the upregulated H. ducreyi genes were found to be involved in ascorbic acid and anaerobic metabolism and inorganic ion/nutrient transport. The top 20 significantly expressed human pathways showed that all were involved in immune responses. We generated a bipartite network for interactions between host and bacterial gene transcription; multiple positively correlated networks contained H. ducreyi genes involved in anaerobic metabolism and host genes involved with the immune response. Metabolomic studies showed that pustule and wounded samples had different metabolite compositions; the top ion pathway involved ascorbate and aldarate metabolism, which correlated with the H. ducreyi transcriptional response and upregulation of host genes involved in ascorbic acid recycling. These data show that an interactome exists between H. ducreyi and the human host and suggest that H. ducreyi exploits the metabolic niche created by the host immune response.IMPORTANCE Dual RNA sequencing (RNA-seq) offers the promise of determining an interactome at a transcriptional level between a bacterium and the host but has yet to be done on any bacterial infection in human tissue. We performed dual RNA-seq and metabolomics analyses on wounded and infected sites following experimental infection of the arm with H. ducreyi Our results suggest that H. ducreyi survives in an abscess by utilizing l-ascorbate as an alternative carbon source, possibly taking advantage of host ascorbic acid recycling, and that H. ducreyi also adapts by upregulating genes involved in anaerobic metabolism and inorganic ion and nutrient transport. To our knowledge, this is the first description of an interaction network between a bacterium and the human host at a site of infection

The thermodynamics of computational copying in biochemical systems

Living cells use readout molecules to record the state of receptor proteins, similar to measurements or copies in typical computational devices. But is this analogy rigorous? Can cells be optimally efficient, and if not, why? We show that, as in computation, a canonical biochemical readout network generates correlations; extracting no work from these correlations sets a lower bound on dissipation. For general input, the biochemical network cannot reach this bound, even with arbitrarily slow reactions or weak thermodynamic driving. It faces an accuracy-dissipation trade-off that is qualitatively distinct from and worse than implied by the bound, and more complex steady-state copy processes cannot perform better. Nonetheless, the cost remains close to the thermodynamic bound unless accuracy is extremely high. Additionally, we show that biomolecular reactions could be used in thermodynamically optimal devices under exogenous manipulation of chemical fuels, suggesting an experimental system for testing computational thermodynamics.Comment: Accepted versio

Mechanism Deduction from Noisy Chemical Reaction Networks

We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semi-accurate but efficient electronic structure calculations. It is designed to (i) accelerate the automated exploration of such networks, and (ii) cope with model-inherent errors in electronic structure calculations on elementary reaction steps. We developed and implemented KiNetX to possess three features. First, KiNetX evaluates the kinetic relevance of every species in a (yet incomplete) reaction network to confine the search for new elementary reaction steps only to those species that are considered possibly relevant. Second, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to reduce a complex network graph to a comprehensible mechanism. Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual rate constants (derived from relative free energies), which allows us to systematically select the most efficient electronic structure model for each elementary reaction given a predefined accuracy. The novelty of KiNetX consists in the rigorous propagation of correlated free-energy uncertainty through all steps of our kinetic analyis. To examine the performance of KiNetX, we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding chemical logic into their underlying graph structure. AutoNetGen allows us to consider a vast number of distinct chemistry-like scenarios and, hence, to discuss assess the importance of rigorous uncertainty propagation in a statistical context. Our results reveal that KiNetX reliably supports the deduction of product ratios, dominant reaction pathways, and possibly other network properties from semi-accurate electronic structure data.Comment: 36 pages, 4 figures, 2 table

Transcriptome pathways unique to dehydration tolerant relatives of modern wheat

Among abiotic stressors, drought is a major factor responsible for dramatic yield loss in agriculture. In order to reveal differences in global expression profiles of drought tolerant and sensitive wild emmer wheat genotypes, a previously deployed shock-like dehydration process was utilized to compare transcriptomes at two time points in root and leaf tissues using the Affymetrix GeneChip(R) Wheat Genome Array hybridization. The comparison of transcriptomes reveal several unique genes or expression patterns such as differential usage of IP(3)-dependent signal transduction pathways, ethylene- and abscisic acid (ABA)-dependent signaling, and preferential or faster induction of ABA-dependent transcription factors by the tolerant genotype that distinguish contrasting genotypes indicative of distinctive stress response pathways. The data also show that wild emmer wheat is capable of engaging known drought stress responsive mechanisms. The global comparison of transcriptomes in the absence of and after dehydration underlined the gene networks especially in root tissues that may have been lost in the selection processes generating modern bread wheats

Nanoscale Electrodes by Conducting Atomic Force Microscopy: Oxygen Reduction Kinetics at the Pt|CsHSO_4 Interface

We quantitatively characterized oxygen reduction kinetics at the nanoscale Pt|CsHSO_4 interface at ~150 °C in humidified air using conducting atomic force microscopy (AFM) in conjunction with AC impedance spectroscopy and cyclic voltammetry. From the impedance measurements, oxygen reduction at Pt|CsHSO_4 was found to comprise two processes, one displaying an exponential dependence on overpotential and the other only weakly dependent on overpotential. Both interfacial processes displayed near-ideal capacitive behavior, indicating a minimal distribution in the associated relaxation time. Such a feature is taken to be characteristic of a nanoscale interface in which spatial averaging effects are absent and, furthermore, allows for the rigorous separation of multiple processes that would otherwise be convoluted in measurements using conventional macroscale electrode geometries. The complete current-voltage characteristics of the Pt|CsHSO_4 interface were measured at various points across the electrolyte surface and reveal a variation of the oxygen reduction kinetics with position. The overpotential-activated process, which dominates at voltages below -1 V, was interpreted as a charge-transfer reaction. Analysis of six different sets of Pt|CsHSO_4 experiments, within the Butler-Volmer framework, yielded exchange coefficients (α) for charge transfer ranging from 0.1 to 0.6 and exchange currents (i_0) spanning 5 orders of magnitude. The observed counter-correlation between the exchange current and exchange coefficient indicates that the extent to which the activation barrier decreases under bias (as reflected in the value of α) depends on the initial magnitude of that barrier under open circuit conditions (as reflected in the value of i_0). The clear correlation across six independent sets of measurements further indicates the suitability of conducting AFM approaches for careful and comprehensive study of electrochemical reactions at electrolyte-metal-gas boundaries