Daniel Crowther

Characterization of the human patatin-like phospholipase family

Several publications have described biological roles for human patatin-like phospholipases (PNPLAs) in the regulation of adipocyte differentiation. Here, we report on the characterization and expression profiling of 10 human PNPLAs. A variety of bioinformatics approaches were used to identify and characterize all PNPLAs encoded by the human genome. The genes described represent a divergent family, most with a highly conserved ortholog in several mammalian species. In silico characterization predicts that two of the genes function as integral membrane proteins and are regulated by cAMP/cGMP. A structurally guided protein alignment of the patatin-like domain identifies a number of conserved residues in all family members. Quantitative PCR was used to determine the expression profile of each family member. Affymetrix-based profiling of a human preadipocyte cell line identified several members that are differentially regulated during cell differentiation. Cumulative data suggest that patatin-like genes normally expressed at very low levels are induced in response to environmental signals. Given the observed conservation of the patatin fold and lipase motif in all human PNPLAs, a single nomenclature to describe the PNPLA family is proposed.

Effects of feeding and body weight loss on the 1H-NMR-based urine metabolic profiles of male Wistar Han Rats: Implications for biomarker discovery

For almost two decades, 1H-NMR spectroscopy has been used as an ‘open’ system to study the temporal changes in the biochemical composition of biofluids, including urine, in response to adverse toxic events. Many of these in vivo studies have reported changes in individual metabolites and patterns of metabolites that correlated with toxicological changes. However, many of the proposed novel biomarkers are common to a number of different types of toxicity. These may therefore reflect non-specific effects of toxicity, such as weight loss, rather than a specific pathology. A study was carried out to investigate the non-specific effects on urinary metabolite profiles by administering four hepatotoxic compounds, as a single dose, to rats at two dose levels: hydrazine hydrate (0.06 or 0.08 g kg−1), 1,2-dimethylhydrazine (0.1 or 0.3 g kg−1), α-napthylisothiocyanate (0.1 or 0.15 g kg−1) and carbon tetrachloride (1.58 or 3.16 g kg−1). The study included weight-matched control animals along with those that were dosed, which were then ‘pair-fed’ with the treated animals so they achieved a similar weight loss. The urinary metabolite profiles were investigated over time using 1H-NMR spectroscopy and compared with the pathology from the same animals. The temporal changes were analysed statistically using multivariate statistical data analysis including principal component analysis, partial least squares, parallel factor analysis and Fishers criteria. A number of metabolites associated with energy metabolism or which are partially dietary in origin, such as creatine, creatinine, tricarboxylic acid (TCA) cycle intermediates, phenylacetylglycine, fumarate, glucose, taurine, fatty acids and N-methylnicotinamide, showed altered levels in the urine of treated and pair-fed animals. Many of these changes correlated well with weight loss. Interestingly, there was no increase in ketone bodies (acetate and β-hydroxybutyrate), which might be expected if energy metabolism was switched from glycolysis to fatty acid β-oxidation. In some instances, the metabolites that changed were considered to be non-specific markers of toxicity, but were also identified as markers of a specific type of toxicity. For example, taurine was raised significantly in carbon tetrachloride-treated animals but reduced in the pair-fed group. However, raised urinary bile acid levels were only seen after α-napthylisothiocyanate treatment. The methodology, statistical analysis used and the data generated will help improve the identification of specific markers or patterns of urinary markers of specific toxic effects.

Peak alignment of urine NMR spectra using fuzzy warping.

Proton nuclear magnetic resonance (1H NMR) spectroscopic analysis of mixtures has been used extensively for a variety of applications ranging from the analysis of plant extracts, wine, and food to the evaluation of toxicity in animals. For example, NMR analysis of urine samples has been used extensively for biomarker discovery and, more simply, for the construction of classification models of toxicity, disease, and biochemical phenotype. However, NMR spectra of complex mixtures typically show unwanted local peak shifts caused by matrix and instrument variability, which must be compensated for prior to statistical analysis and interpretation of the data. One approach is to align the spectral peaks across the data set. An efficient and fast warping algorithm is required as the signals typically contain ca. 32,000-64,000 data points and there can be several thousand spectra in a data set. As demonstrated in our study, the iterative fuzzy warping algorithm fulfills these requirements and can be used on-line for an alignment of the NMR spectra. Correlation coefficients between the aligned and target spectra are used as the evaluation function for the algorithm, and its performance is compared with those of other published warping methods.

Applications of microarrays in the pharmaceutical industry

Microarrays provide the ability to measure the expression of thousands of genes in parallel. From target discovery through to uses in the clinic, microarrays are having an enormous impact on research in the pharmaceutical industry. In particular, microarrays have applications in genome annotation, they contribute to improving our disease understanding and can be used in the drug development pipeline to improve selection of biological targets and lead compounds.

Gene regulatory network of human adipocyte differentiation

Background In this article we demonstrate novel pre-processing methods to reduce data dimensionality of human adipocyte differentiation microarray data. Genetic networks of the insulin receptor family, ppar family, fox family, cebp family mef2, fabp, add1 and klf, and probes with highly significant change in gene expression level were learned separately using a Bayesian frame work. The extracted networks were validation of genetic network against many publicly available and as well as in house interaction and literature databases available at GSK.

Search-space reduction of a non-redundant peptide database

Peptide mass fingerprinting and database searching with tandem mass spectrometry are two methods commonly employed to identify proteins in a sample. However, up to 90% of peptides can remain unidentified. In this paper, a search-space filter using amino acids identified by a novel de nova methodology is presented. This provides a high-accuracy set of amino acid predictions through exploiting the internal fragmentation of amino acid chains during tandem mass spectrometry. These predictions are used to reduce the number of peptides considered from a non-redundant peptide database. The presence of one confirmed amino acid can be used to reduce the search-database size by between 33% (leucine) and 83% (tryptophan). One or more accurate amino acid identifications are made in 18% of simulated and 9% of experimental peptide spectra considered. Given the large proportion of currently unidentified peptides, this method represents a useful tool for increasing peptide identification rates.