High similarity of phylogenetic profiles of rate-limiting enzymes with inhibitory relation in Human, Mouse, Rat, budding Yeast and E. coli

Background: The phylogenetic profile is widely used to characterize functional linkage and conservation between proteins without amino acid sequence similarity. To survey the conservative regulatory properties of rate-limiting enzymes (RLEs) in metabolic inhibitory network across different species, we define the enzyme inhibiting pair as: where the first enzyme in a pair is the inhibitor provider and the second is the target of the inhibitor. Phylogenetic profiles of enzymes in the inhibiting pairs are further generated to measure the functional linkage of these enzymes during evolutionary history. Results: We find that the RLEs generate, on average, over half of all in vivo inhibitors in each surveyed model organism. And these inhibitors inhibit on average over 85% targets in metabolic inhibitory network and cover the majority of targets of cross-pathway inhibiting relations. Furthermore, we demonstrate that the phylogenetic profiles of the enzymes in inhibiting pairs in which at least one enzyme is rate-limiting often show higher similarities than those in common inhibiting enzyme pairs. In addition, RLEs, compared to common metabolic enzymes, often tend to produce ADP instead of AMP in conservative inhibitory networks. Conclusions: Combined with the conservative roles of RLEs in their efficiency in sensing metabolic signals and transmitting regulatory signals to the rest of the metabolic system, the RLEs may be important molecules in balancing energy homeostasis via maintaining the ratio of ATP to ADP in living cells. Furthermore, our results indicate that similarities of phylogenetic profiles of enzymes in the inhibiting enzyme pairs are not only correlated with enzyme topological importance, but also related with roles of the enzymes in metabolic inhibitory network. © 2011 licensee BioMed Central Ltd

Human liver rate-limiting enzymes influence metabolic flux via branch points and inhibitors

Background: Rate-limiting enzymes, because of their relatively low velocity, are believed to influence metabolic flux in pathways. To investigate their regulatory role in metabolic networks, we look at the global organization and interactions between rate-limiting enzymes and compounds such as branch point metabolites and enzyme inhibitors in human liver. Results: Based on 96 rate-limiting enzymes and 132 branch point compounds from human liver, we found that rate-limiting enzymes surrounded 76.5% of branch points. In a compound conversion network from human liver, the 128 branch points involved showed a dramatically higher average degree, betweenness centrality and closeness centrality as a whole. Nearly half of the in vivo inhibitors were products of rate-limiting enzymes, and covered 75.34% of the inhibited targets in metabolic inhibitory networks. Conclusion: From global topological organization, rate-limiting enzymes as a whole surround most of the branch points; so they can influence the flux through branch points. Since nearly half of the in vivo enzyme inhibitors are produced by rate-limiting enzymes in human liver, these enzymes can initiate inhibitory regulation and then influence metabolic flux through their natural products. © 2009 Zhao and Qu; licensee BioMed Central Ltd

Stochasticity in Protein Levels Drives Colinearity of Gene Order in Metabolic Operons of Escherichia coli

Gene order in some bacterial metabolic operons reflects ordering in the metabolic pathway. That this is true uniquely for operons expressed at low levels highlights the selective importance of fluctuations in protein levels

Computational identification of significantly regulated metabolic reactions by integration of data on enzyme activity and gene expression

ope

Seeing the forest for the trees : retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed

The architecture of regulatory network of metabolism

The thesis focus on the modularity of metabolic network and foremost on the architecture of regulatory network representing direct regulatory interactions between metabolites and enzymes. I focus on the "modularity measure" in my first work. Modularity measure is quantitative measure of network modularity commonly used for module identification. It was showed that algorithms using this measure can produce modules that are composed of two clearly pronounced sub-modules. Maximum size of module for which there is a risk that is is composed of two sub-modules is called resolution limit of modularity measure. In my first work I generalize resolution limit of modularity measure. The generalized version provide insight to the origin of resolution limit in the null-model used by modularity measure. Moreover it is showed that the risk of omitting of sub-modular structures applies for bigger modules than mentioned in the original publication. The second work is focused on the question how does the modular structure of E. coli metabolic network change if we add regulatory interactions. I find that the modularity of modular core of network slightly increase after regulatory edges addition. The modularity increase is significant with respect to randomized ensemble of regulatory networks. Identified modules...Předkládaná disertační práce se zabývá modularitou metabolických sítí a především architekturou regulační sítě metabolismu, která reprezentuje přímé regulační interakce mezi metabolity a enzymy. V první práci se zabývám problematikou tzv. "modularity measure", což je kvantitativní míra modularity sítě používaná pro účely identifikace modulů. Bylo zjištěno, že při maximalizaci této veličiny v síti může dojít k chybnému sloučení dvou jednoznačne vyjádřených modulů v jeden. Maximální velikost modulu u kterého existuje riziko, že je tvořen dvěma moduly je známa jako rozlišovací limit modularity measure. V mé první práci je tento rozlišovací limit zobecněn, což umožňuje nahlédnout jeho podstatu v použití nulového modelu. Zároveň je zde ukázáno, že riziko chybného sloučení existuje i v případě větších modulů, než bylo uváděno v původní práci. Druhá práce je zaměřena na otázku, jak se změní modularita metabolické sítě E.coli po přidání regulačních vazeb. Bylo zde ukázáno, že modularita mírně nicméně signifikantně vzroste, zaměříme-li se na modulární jádro sítě. Identifikované moduly jsou funkčně interpretovatelné jako regulačně autonomí části metabolismu. Zvýšení modularity vzhledem k nulovému modelu lze považovat za nepřímý důsledek potřeby lokální regulace některých částí metabolické sítě. Vznik...Department of Philosophy and History of ScienceKatedra filosofie a dějin přírodních vědFaculty of SciencePřírodovědecká fakult

MECHANISMS OF CROSS-TALK IN SECONDARY METABOLISM PATHWAYS IN ARABIDOPSIS REVEALED BY INTEGRATIVE FUNCTIONAL GENOMICS

Ph.DDOCTOR OF PHILOSOPH

Regulation of metabolic networks by small molecule metabolites

Abstract Background The ability to regulate metabolism is a fundamental process in living systems. We present an analysis of one of the mechanisms by which metabolic regulation occurs: enzyme inhibition and activation by small molecules. We look at the network properties of this regulatory system and the relationship between the chemical properties of regulatory molecules. Results We find that many features of the regulatory network, such as the degree and clustering coefficient, closely match those of the underlying metabolic network. While these global features are conserved across several organisms, we do find local differences between regulation in E. coli and H. sapiens which reflect their different lifestyles. Chemical structure appears to play an important role in determining a compounds suitability for use in regulation. Chemical structure also often determines how groups of similar compounds can regulate sets of enzymes. These groups of compounds and the enzymes they regulate form modules that mirror the modules and pathways of the underlying metabolic network. We also show how knowledge of chemical structure and regulation could be used to predict regulatory interactions for drugs. Conclusion The metabolic regulatory network shares many of the global properties of the metabolic network, but often varies at the level of individual compounds. Chemical structure is a key determinant in deciding how a compound is used in regulation and for defining modules within the regulatory system.</p