The spectrum of endogenous human chromogranin A-derived peptides identified using a modified proteomic strategy.

The hypothesis that chromogranin A (CgA), a protein of neuroendocrine cell secretory granules, may be a precursor of biologically active peptides, rests on observed activities of peptide fragments largely produced by exogenous protease digestion of the bovine protein. Here we have adopted a modified proteomic strategy to isolate and characterise human CgA-derived peptides produced by endogenous prohormone convertases. Initial focus was on an insulinoma as previous studies have shown that CgA is rapidly processed in pancreatic beta cells and that tumours arising from these express appropriate prohormone convertases. Eleven novel peptides were identified arising from processing at both monobasic and dibasic sites and processing was most evident in the C-terminal domain of the protein. Some of these peptides were identified in endocrine tumours, such as mid-gut carcinoid and phaeochromocytoma, which arise from endocrine cells of different phenotype and in different anatomical sites. Two of the most interesting peptides, GR-44 and ER-37, representing the C-terminal region of CgA, were found to be amidated. These data would imply that the intact protein is C-terminally amidated and that these peptides are probably biologically active. The spectrum of novel CgA-derived peptides, described in the present study, should provide a basis for biological evaluation of authentic entities.

Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.

The Galactic metallicity gradient

We have previously published intermediate to hi,oh resolution spectroscopic observations of approximately 80 early B-type main-sequence stars situated in 19 Galactic open clusters/associations with Galactocentric distances distributed over 6 less than or equal to R-g less than or equal to 18 kpc. This current study collates and re-analyses these equivalent- width datasets using LTE and non-LTE model atmosphere techniques, in order to determine the stellar atmospheric parameters and abundance estimates for C, N, O, Mg, Al and Si. The latter should be representative of the present-day Galactic interstellar medium. Our extensive observational dataset permits the identification of sub-samples of stars with similar atmospheric parameters and of homogeneous subsets of lines. As such, this investigation represents the most extensive and systematic study of its kind to date. We conclude that the distribution of light elements (CI O, Mg & Si) in the Galactic disk can be represented by a linear, radial gradient of -0.07 +/- 0.01 dex kpc(-1) Our results for nitrogen and oxygen viz. (-0.09 +/- 0.01 dex kpc(-1) and -0.067 +/- 0.008 dex kpc(-1)) are in excellent agreement with that found from the study of HII regions. We have also examined our datasets for evidence of an abrupt discontinuity in the metallicity of the Galactic disk near a Galactocentric distance of 10 kpc (see Twarog et al. 1997). However, there is no evidence to suggest that our data would be better fitted with a two-zone model. Moreover, we observe a N/O gradient of -0.04 +/- 0.02 dex kpc(-1) which is consistent with that found for other spiral galaxies (Vila- Costas gr Edmunds 1993).

Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling

Genome-scale metabolic models promise important insights into cell function. However, the definition of pathways and functional network modules within these models, and in the biochemical literature in general, is often based on intuitive reasoning. Although mathematical methods have been proposed to identify modules, which are defined as groups of reactions with correlated fluxes, there is a need for experimental verification. We show here that multivariate statistical analysis of the NMR-derived intra- and extracellular metabolite profiles of single-gene deletion mutants in specific metabolic pathways in the yeast Saccharomyces cerevisiae identified outliers whose profiles were markedly different from those of the other mutants in their respective pathways. Application of flux coupling analysis to a metabolic model of this yeast showed that the deleted gene in an outlying mutant encoded an enzyme that was not part of the same functional network module as the other enzymes in the pathway. We suggest that metabolomic methods such as this, which do not require any knowledge of how a gene deletion might perturb the metabolic network, provide an empirical method for validating and ultimately refining the predicted network structure.

Neuropeptide-like protein diversity in phylum Nematoda

This study reports the identification of nematode neuropeptide-like protein (nlp) sequelogs from the GenBank expressed sequence tag (EST) database, using BLAST (Basic Local Alignment Search Tool) search methodology. Search strings derived from peptides encoded by the 45 known Caenorhabatitis elegans nlp genes were used to identify more than 1000 ESTs encoding a total of 26 multi-species nlp sequelogs. The remaining 18 nlps (nlp-4, -16, -24 through -36, -39, -41 and -45) were identified only in C elegans, while the sole EST representative of nlp-23 was from Caenorhabditis remanei. Several ESTs encoding putative antibacterial peptides similar to those encoded by the C elegans genes nlp-24-33 were observed in several parasite species. A novel gene (nlp-46) was identified, encoding a single, amidated dodecapeptide (NIA[I/T]GR[G/A]DG[F/L]RPG) in eight species. Secretory signal peptides were identified in at least one species representing each nlp sequelog, confirming that all 46 nematode nlp genes encode secretory peptides. A random sub-set of C elegans NLPs was tested physiologically in Ascaris suum ovijector and body wall muscle bioassays. None of the peptides tested were able to modulate ovijector activity, while only three displayed measurable myoactivity on somatic body wall muscle. AFAAGWNRamide (from nlp-23) and AVNPFLDSIamide (nlp-3) both produced a relaxation of body wall muscle, while AIPFNGGMYamide (nlp-10) induced a transient contraction. Numerical analyses of nip-encoding ESTs demonstrate that nlp-3, -13, -14, -15 and -18 are amongst the most highly represented transcripts in the dataset. Using available bioinformatics resources, this study delineates the nlp complement of phylum Nematoda, providing a rich source of neuropeptide ligands for deorphanisation of nematode neuropeptide receptors. (C) 2008 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Spectral induced polarization signatures of abiotic FeS precipitation

In recent years, geophysical methods have been shown to be sensitive to microbial-induced mineralization processes. The spectral induced-polarization (SIP) method appears to be very promising for monitoring mineralization and microbial processes. With this work, we study the links of mineralization and SIP signals, in the absence of microbial activity. We recorded the SIP response during abiotic FeS precipitation. We show that the SIP signals are diagnostic of FeS mineralization and can be differentiated from SIP signals from biomineralization processes. More specifically, the imaginary conductivity shows almost linear dependence on the amount of FeS precipitating out of solution, above the threshold value 0.006 gr under our experimental conditions. This research has direct implications for the use of the SIP method as a monitoring and decision-making tool for sustainable remediation of metals in contaminated soils and groundwater.

Glucocorticoid receptor β and histone deacetylase 1 and 2 expression in the airways of severe asthma.

Rationale Upregulation of glucocorticoid receptor ß (GRß) has been implicated in steroid resistance in severe asthma, although previous studies are conflicting. GRß has been proposed as a dominant negative isoform of glucocorticoid receptor a (GRa) but it has also been suggested that GRß can cause steroid resistance via reduced expression of histone deacetylase 2 (HDAC2), a key regulator of steroid responsiveness in the airway.


Objectives To examine GRß, GRa, HDAC1 and HDAC2 expression at transcript and protein levels in bronchial biopsies from a large series of patients with severe asthma, and to compare the findings with those of patients with mild to moderate asthma and healthy volunteers.


Methods Bronchoscopic study in two UK centres with real-time PCR and immunohistochemistry performed on biopsies, western blotting of bronchial epithelial cells and immunoprecipitation with anti-GRß antibody.


Measurements and main results Protein and mRNA expression for GRa and HDAC2 did not differ between groups. GRß mRNA was detected in only 13 of 73 samples (seven patients with severe asthma), however immunohistochemistry showed widespread epithelial staining in all groups. Western blotting of bronchial epithelial cells with GRß antibody detected an additional ‘cross-reacting’ protein, identified as clathrin. HDAC1 expression was increased in patients with severe asthma compared with healthy volunteers.


Conclusions GRß mRNA is expressed at low levels in a minority of patients with severe asthma. HDAC1 and HDAC2 expression was not downregulated in severe asthma. These data do not support upregulated GRß and resultant reduced HDAC expression as the principal mechanism of steroid resistance in severe asthma. Conflicting GRß literature may be explained in part by clathrin cross-reactivity with commercial antibodies.

Increasing the accuracy of Nitrogen Dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics.

Nitrogen Dioxide (NO2) is known to act as an environmental trigger for many respiratory illnesses. As a pollutant it is difficult to map accurately, as concentrations can vary greatly over small distances. In this study three geostatistical techniques were compared, producing maps of NO2 concentrations in the United Kingdom (UK). The primary data source for each technique was NO2 point data, generated from background automatic monitoring and background diffusion tubes, which are analysed by different laboratories on behalf of local councils and authorities in the UK. The techniques used were simple kriging (SK), ordinary kriging (OK) and simple kriging with a locally varying mean (SKlm). SK and OK make use of the primary variable only. SKlm differs in that it utilises additional data to inform prediction, and hence potentially reduces uncertainty. The secondary data source was Oxides of Nitrogen (NOx) derived from dispersion modelling outputs, at 1km x 1km resolution for the UK. These data were used to define the locally varying mean in SKlm, using two regression approaches: (i) global regression (GR) and (ii) geographically weighted regression (GWR). Based upon summary statistics and cross-validation prediction errors, SKlm using GWR derived local means produced the most accurate predictions. Therefore, using GWR to inform SKlm was beneficial in this study.