Diffusive coupling can discriminate between similar reaction mechanisms in an allosteric enzyme system

<p>Abstract</p> <p>Background</p> <p>A central question for the understanding of biological reaction networks is how a particular dynamic behavior, such as bistability or oscillations, is realized at the molecular level. So far this question has been mainly addressed in well-mixed reaction systems which are conveniently described by ordinary differential equations. However, much less is known about how molecular details of a reaction mechanism can affect the dynamics in diffusively coupled systems because the resulting partial differential equations are much more difficult to analyze.</p> <p>Results</p> <p>Motivated by recent experiments we compare two closely related mechanisms for the product activation of allosteric enzymes with respect to their ability to induce different types of reaction-diffusion waves and stationary Turing patterns. The analysis is facilitated by mapping each model to an associated complex Ginzburg-Landau equation. We show that a sequential activation mechanism, as implemented in the model of Monod, Wyman and Changeux (MWC), can generate inward rotating spiral waves which were recently observed as glycolytic activity waves in yeast extracts. In contrast, in the limiting case of a simple Hill activation, the formation of inward propagating waves is suppressed by a Turing instability. The occurrence of this unusual wave dynamics is not related to the magnitude of the enzyme cooperativity (as it is true for the occurrence of oscillations), but to the sensitivity with respect to changes of the activator concentration. Also, the MWC mechanism generates wave patterns that are more stable against long wave length perturbations.</p> <p>Conclusions</p> <p>This analysis demonstrates that amplitude equations, which describe the spatio-temporal dynamics near an instability, represent a valuable tool to investigate the molecular effects of reaction mechanisms on pattern formation in spatially extended systems. Using this approach we have shown that the occurrence of inward rotating spiral waves in glycolysis can be explained in terms of an MWC, but not with a Hill mechanism for the activation of the allosteric enzyme phosphofructokinase. Our results also highlight the importance of enzyme oligomerization for a possible experimental generation of Turing patterns in biological systems.</p

Ensemble Modeling for Aromatic Production in Escherichia coli

Ensemble Modeling (EM) is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning

The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles

Background: The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency. Results: Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems. Conclusion: Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network

TCF4 sequence variants and mRNA levels are associated with neurodevelopmental characteristics in psychotic disorders

TCF4 is involved in neurodevelopment, and intergenic and intronic variants in or close to the TCF4 gene have been associated with susceptibility to schizophrenia. However, the functional role of TCF4 at the level of gene expression and relationship to severity of core psychotic phenotypes are not known. TCF4 mRNA expression level in peripheral blood was determined in a large sample of patients with psychosis spectrum disorders (n=596) and healthy controls (n=385). The previously identified TCF4 risk variants (rs12966547 (G), rs9960767 (C), rs4309482 (A), rs2958182 (T) and rs17512836 (C)) were tested for association with characteristic psychosis phenotypes, including neurocognitive traits, psychotic symptoms and structural magnetic resonance imaging brain morphometric measures, using a linear regression model. Further, we explored the association of additional 59 single nucleotide polymorphisms (SNPs) covering the TCF4 gene to these phenotypes. The rs12966547 and rs4309482 risk variants were associated with poorer verbal fluency in the total sample. There were significant associations of other TCF4 SNPs with negative symptoms, verbal learning, executive functioning and age at onset in psychotic patients and brain abnormalities in total sample. The TCF4 mRNA expression level was significantly increased in psychosis patients compared with controls and positively correlated with positive- and negative-symptom levels. The increase in TCF4 mRNA expression level in psychosis patients and the association of TCF4 SNPs with core psychotic phenotypes across clinical, cognitive and brain morphological domains support that common TCF4 variants are involved in psychosis pathology, probably related to abnormal neurodevelopment

Initiation of rrn transcription in chloroplasts of Euglena gracilis bacillaris

The site of initiation of chloroplast rRNA synthesis was determined by Sl-mapping and by sequencing primary rRNA transcripts specifically labeled at their 5′-end. Transcription initiates at a single site 53 nucleotides upstream of the 5'-end of the mature 16S rRNA under all growth conditions examined. The initiation site is within a DNA sequence that is highly homologous to and probably derived from a tRNA gene-region located elsewhere in the chloroplast genome. A nearly identical sequence (102 of 103 nucleotides) is present near the replication origin. The near identity of the two sequences suggests a common mode for control of transcription of the rRNA genes and initiation of chloroplast DNA replication. The related sequence in the tRNA gene-region does not appear to serve as a transcript initiation site.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46967/1/294_2004_Article_BF00521275.pd

C9orf72-mediated ALS and FTD: multiple pathways to disease

The discovery that repeat expansions in the C9orf72 gene are a frequent cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) has revolutionized our understanding of these diseases. Substantial headway has been made in characterizing C9orf72-mediated disease and unravelling its underlying aetiopathogenesis. Three main disease mechanisms have been proposed: loss of function of the C9orf72 protein and toxic gain of function from C9orf72 repeat RNA or from dipeptide repeat proteins produced by repeat-associated non-ATG translation. Several downstream processes across a range of cellular functions have also been implicated. In this article, we review the pathological and mechanistic features of C9orf72-associated FTD and ALS (collectively termed C9FTD/ALS), the model systems used to study these conditions, and the probable initiators of downstream disease mechanisms. We suggest that a combination of upstream mechanisms involving both loss and gain of function and downstream cellular pathways involving both cell-autonomous and non-cell-autonomous effects contributes to disease progression