John Connelly

Bio: John Connelly is an academic researcher from Imperial College London. The author has contributed to research in topics: Proton NMR & Nephrotoxicity. The author has an hindex of 10, co-authored 10 publications receiving 3014 citations.

Metabonomics: a platform for studying drug toxicity and gene function

Abstract: The later that a molecule or molecular class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery programme. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery-and-development process.

Integrated Metabonomic Analysis of the Multiorgan Effects of Hydrazine Toxicity in the Rat

Abstract: Hydrazine is a model toxin that induces both hepatotoxic and neurotoxic effects in experimental animals. The direct biochemical effects of hydrazine in kidney, liver, and brain tissue were assessed in male Sprague−Dawley rats using magic angle spinning nuclear magnetic resonance (NMR) spectroscopy. A single dose of hydrazine (90 mg/kg) resulted in changes to the biochemical composition of the liver after 24 h including an increase in triglycerides and β-alanine, together with a decrease in hepatic glycogen, glucose, choline, taurine, and trimethylamine-N-oxide (TMAO). From histopathology measurements of liver tissue, minimal to mild hepatocyte alteration was observed in all animals at 24 h. The NMR spectra of the renal cortex at 24 h after dosing were dominated by a marked increase in the tissue concentration of 2-aminoadipate (2-AA) and β-alanine, concomitant with depletions in TMAO, myo-inositol, choline, taurine, glutamate, and lysine. No alteration to the NMR spectral profile of the substantia nigra w...

Chemometric models for toxicity classification based on NMR spectra of biofluids

Abstract: 1H NMR spectroscopic and pattern recognition (PR)-based methods were used to investigate the biochemical variability in urine obtained from control rats and from rats treated with a hydrazine (a model hepatotoxin) or HgCl(2) (a model renal cortical toxin). The 600 MHz (1)H NMR spectra of urine samples obtained from vehicle- or toxin-treated Han-Wistar (HW) and Sprague-Dawley (SD) rats were acquired, and principal components analysis (PCA) and soft independent modeling of class analogy (SIMCA) analysis were used to investigate the (1)H NMR spectral data. Variation and strain differences in the biochemical composition of control urine samples were assessed. Control urine (1)H NMR spectra obtained from the two rat strains appeared visually similar. However, chemometric analysis of the control urine spectra indicated that HW rat urine contained relatively higher concentrations of lactate, acetate, and taurine and lower concentrations of hippurate than SD rat urine. Having established the extent of biochemical variation in the two populations of control rats, PCA was used to evaluate the metabolic effects of hydrazine and HgCl(2) toxicity. Urinary biomarkers of each class of toxicity were elucidated from the PC loadings and included organic acids, amino acids, and sugars in the case of mercury, while levels of taurine, beta-alanine, creatine, and 2-aminoadipate were elevated after hydrazine treatment. SIMCA analysis of the data was used to build predictive models (from a training set of 416 samples) for the classification of toxicity type and strain of rat, and the models were tested using an independent set of urine samples (n = 124). Using models constructed from the first three PCs, 98% of the test samples were correctly classified as originating from control, hydrazine-treated, or HgCl(2)-treated rats. Furthermore, this method was sensitive enough to predict the correct strain of the control samples for 79% of the data, based upon the class of best fit. Incorporation of these chemometric methods into automated NMR-based metabonomics analysis will enable on-line toxicological assessment of biofluids and will provide a tool for probing the mechanistic basis of organ toxicity.

Development of a model for classification of toxin-induced lesions using 1H NMR spectroscopy of urine combined with pattern recognition.

Abstract: Pattern recognition approaches were developed and applied to the classification of 600 MHz 1 H NMR spectra of urine from rats dosed with compounds that induced organ-specific damage in either the liver or kidney Male rats were separated into groups (n = 5) and each treated with one of the following compounds; adriamycin, allyl alcohol, 2-bromoethanamine hydrobromide, hexachlorobutadiene, hydrazine, lead acetate, mercury II chloride, puromycin aminonucleoside, sodium chromate, thioacetamide, 1,1,2-trichloro-3,3,3-trifluoro-1-propene or dose vehicle Urine samples were collected over a 7 day time-course and analysed using 600 MHz 1 H NMR spectroscopy Each NMR spectrum was data-reduced to provide 256 intensity-related descriptors of the spectra Data corresponding to the periods 8-24 h, 24-32 h and 32-56 h post-dose were first analysed using principal components analysis (PCA) In addition, samples obtained 120-144 h following the administration of adriamycin and puromycin were included in the analysis in order to compensate for the late onset of glomerular toxicity Having established that toxin-related clustering behaviour could be detected in the first three principal components (PCs), three-quarters of the data were used to construct a soft independent modelling of class analogy (SIMCA) model The remainder of the data were used as a test set of the model Only three out of 61 samples in the test set were misclassified Finally as a further test of the model, data from the 1 H NMR spectra of urine from rats that had been treated with uranyl nitrate were used Successful prediction of the toxicity type of the compound was achieved based on NMR urinalysis data confirming the robust nature of the derived model © 1998 John Wiley & Sons, Ltd

'metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological nmr spectroscopic data

Abstract: (1999). 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica: Vol. 29, No. 11, pp. 1181-1189.

Molecular imaging in living subjects: seeing fundamental biological processes in a new light

Abstract: References http://genesdev.cshlp.org/content/17/5/545.full.html#related-urls Article cited in: http://genesdev.cshlp.org/content/17/5/545.full.html#ref-list-1 This article cites 228 articles, 79 of which can be accessed free at: service Email alerting click here top right corner of the article or Receive free email alerts when new articles cite this article sign up in the box at the Collections Topic (33 articles) Molecular Physiology and Metabolism • (98 articles) Cancer and Disease Models • Articles on similar topics can be found in the following collections

Second generation biofuels: high-efficiency microalgae for biodiesel production

Abstract: The use of fossil fuels is now widely accepted as unsustainable due to depleting resources and the accumulation of greenhouse gases in the environment that have already exceeded the “dangerously high” threshold of 450 ppm CO2-e. To achieve environmental and economic sustainability, fuel production processes are required that are not only renewable, but also capable of sequestering atmospheric CO2. Currently, nearly all renewable energy sources (e.g. hydroelectric, solar, wind, tidal, geothermal) target the electricity market, while fuels make up a much larger share of the global energy demand (∼66%). Biofuels are therefore rapidly being developed. Second generation microalgal systems have the advantage that they can produce a wide range of feedstocks for the production of biodiesel, bioethanol, biomethane and biohydrogen. Biodiesel is currently produced from oil synthesized by conventional fuel crops that harvest the sun’s energy and store it as chemical energy. This presents a route for renewable and carbon-neutral fuel production. However, current supplies from oil crops and animal fats account for only approximately 0.3% of the current demand for transport fuels. Increasing biofuel production on arable land could have severe consequences for global food supply. In contrast, producing biodiesel from algae is widely regarded as one of the most efficient ways of generating biofuels and also appears to represent the only current renewable source of oil that could meet the global demand for transport fuels. The main advantages of second generation microalgal systems are that they: (1) Have a higher photon conversion efficiency (as evidenced by increased biomass yields per hectare): (2) Can be harvested batch-wise nearly all-year-round, providing a reliable and continuous supply of oil: (3) Can utilize salt and waste water streams, thereby greatly reducing freshwater use: (4) Can couple CO2-neutral fuel production with CO2 sequestration: (5) Produce non-toxic and highly biodegradable biofuels. Current limitations exist mainly in the harvesting process and in the supply of CO2 for high efficiency production. This review provides a brief overview of second generation biodiesel production systems using microalgae.

Innovation: Metabolomics: the apogee of the omics trilogy

Abstract: Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine.

Metabonomics: a platform for studying drug toxicity and gene function

Abstract: The later that a molecule or molecular class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery programme. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery-and-development process.