Determination of metabolic fluxes in a non-steady-state system.

Estimation of fluxes through metabolic networks from redistribution patterns of (13)C has become a well developed technique in recent years. However, the approach is currently limited to systems at metabolic steady-state; dynamic changes in metabolic fluxes cannot be assessed. This is a major impediment to understanding the behaviour of metabolic networks, because steady-state is not always experimentally achievable and a great deal of information about the control hierarchy of the network can be derived from the analysis of flux dynamics. To address this issue, we have developed a method for estimating non-steady-state fluxes based on the mass-balance of mass isotopomers. This approach allows multiple mass-balance equations to be written for the change in labelling of a given metabolite pool and thereby permits over-determination of fluxes. We demonstrate how linear regression methods can be used to estimate non-steady-state fluxes from these mass balance equations. The approach can be used to calculate fluxes from both mass isotopomer and positional isotopomer labelling information and thus has general applicability to data generated from common spectrometry- or NMR-based analytical platforms. The approach is applied to a GC-MS time-series dataset of (13)C-labelling of metabolites in a heterotrophic Arabidopsis cell suspension culture. Threonine biosynthesis is used to demonstrate that non-steady-state fluxes can be successfully estimated from such data while organic acid metabolism is used to highlight some common issues that can complicate flux estimation. These include multiple pools of the same metabolite that label at different rates and carbon skeleton rearrangements.

Application of metabolite profiling to the identification of traits in a population of tomato introgression lines.

Naturally occurring variation in wild species can be used to increase the genetic diversity of cultivated crops and improve agronomic value. Populations of introgression lines carrying wild species alleles afford an opportunity to identify traits associated with the introgressed regions, and facilitate characterization of the biochemistry and genetics underlying these phenotypes. Understanding plant metabolic pathways and the interactions between genes, phenotype, and environment is fundamental to functional genomics. Successful analysis of the complex network of plant metabolism requires analytical methods able to record information on as many metabolites as possible. Metabolite profiling is used to provide a snapshot of the metabolome in samples which differ in a known factor such as genetic background. Differences between the metabolite profiles can identify those metabolites/metabolic pathways affected by the introgression and allow genetic maps for metabolic alterations to be established. A Time-of-Flight Mass Spectrometry method is presented, with associated data reduction, used for profiling aqueous metabolites fom tomato. Analysis of ripe fruits of two tomato species, Lycopersicon esculentum and L. pennellii, showed differences in the amounts of many metabolites, including organic acids and sugars. Six introgression lines, L. pennellii introgressions within L. esculentum, were also examined and showed that Principal Component Analysis can reveal subtle differences in metabolism of the introgressed lines when compared to their parents.

Bilateral mandibular dentigerous cysts in a dog.

Cysts in the dental cavities of dogs are rare. This report documents the case of a young boxer that was referred after having had treatment for bilateral oral swellings. Dentigerous cysts were tentatively diagnosed. Surgery was performed to aspirate the fluid and remove the first premolars, which were positioned in the cysts. Histopathological examination of tissue collected during the surgical procedure confirmed the diagnosis.