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SummaryTranslation of the transcription factor bZIP11 is repressed by sucrose in a process that involves a highly conserved peptide encoded by the 5¢ leaders of bZIP11 and other plant basic region leucine zipper (bZip) genes. It is likely that a specific signaling pathway operating at physiological sucrose concentrations controls metabolism via a feedback mechanism. In this paper bZIP11 target processes are identified using transiently increased nuclear bZIP11 levels and genome-wide expression analysis. bZIP11 affects the expression of hundreds of genes with proposed functions in biochemical pathways and signal transduction. The expression levels of approximately 80% of the genes tested are not affected by bZIP11 promoter-mediated overexpression of bZIP11. This suggests that <20% of the identified genes appear to be physiologically relevant targets of bZIP11. ASPARAGINE SYNTHETASE1 and PROLINE DEHYDROGENASE2 are among the rapidly activated bZIP11 targets, whose induction is independent of protein translation. Transient expression experiments in Arabidopsis protoplasts show that the bZIP11-dependent activation of the ASPARAGINE SYNTHETASE1 gene is dependent on a G-box element present in the promoter. Increased bZIP11 expression leads to decreased proline and increased phenylalanine levels. A model is proposed in which sugar signals control amino acid levels via the bZIP11 transcription factor.

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To predict ammonia (NH)) volatilization from field-applied manure, factors affecting volatilization following manure application need to be known. A database of field measurements in the Netherlands was analysed to identify factors affecting the volatilization from manure applied to grassland by various techniques, and to quantify their effects. The application techniques were broadcast surface spreading, narrow-band application, and shallow injection. External factors considered were weather conditions, manure characteristics, soil type and soil moisture content, and grass height. Narrow-band application and shallow injection significantly reduced NH) volatilization, compared with broadcast surface spreading. The mean cumulative volatilization for surface spreading was estimated to be 77% of the total ammoniacal nitrogen (TAN) applied, 20% for narrow-band application and 6% for shallow injection. The TAN content of the manure, the manure application rate and the weather conditions significantly influenced the NH) volatilization rate. The volatilization rate increased with an increase in TAN content of the manure, manure application rate, wind speed, radiation, or air temperature. It decreased with an increase in the relative humidity. The identified influencing factors and their magnitude differed with the application technique. Grass height affected NH) volatilization when manure was applied in narrow bands. The results show that external factors need to be taken into account when predicting ammonia volatilization following manure application.

SELDI-TOF-MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of X-omics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a 'mere' test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method.

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data types based on biological/environmental variability around steady state were analyzed to compare the relative information content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions. We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong to metabolites connected with weak interaction strength.

Background Phenylketonuria (PKU) causes irreversible central nervous system damage unless a phenylalanine (PHE) restricted diet with amino acid supplementation is maintained. To prevent growth retardation, a protein/amino acid intake beyond the recommended dietary protein allowance is mandatory. However, data regarding disease and/or diet related changes in body composition are inconclusive and retarded growth and/or adiposity is still reported. The BodPod whole body air-displacement plethysmography method is a fast, safe and accurate technique to measure body composition.