Publications
74 results found
Liebeke M, Garcia-Perez I, Anderson CJ, et al., 2013, Earthworms Produce phytochelatins in Response to Arsenic, PLOS One, Vol: 8, ISSN: 1932-6203
Phytochelatins are small cysteine-rich non-ribosomal peptides that chelate soft metal and metalloid ions, such ascadmium and arsenic. They are widely produced by plants and microbes; phytochelatin synthase genes are alsopresent in animal species from several different phyla, but there is still little known about whether these genes arefunctional in animals, and if so, whether they are metal-responsive. We analysed phytochelatin production by directchemical analysis in Lumbricus rubellus earthworms exposed to arsenic for a 28 day period, and found that arsenicclearly induced phytochelatin production in a dose-dependent manner. It was necessary to measure thephytochelatin metabolite concentrations directly, as there was no upregulation of phytochelatin synthase geneexpression after 28 days: phytochelatin synthesis appears not to be transcriptionally regulated in animals. A furtheruntargetted metabolomic analysis also found changes in metabolites associated with the transsulfuration pathway,which channels sulfur flux from methionine for phytochelatin synthesis. There was no evidence of biologicaltransformation of arsenic (e.g. into methylated species) as a result of laboratory arsenic exposure. Finally, wecompared wild populations of earthworms sampled from the field, and found that both arsenic-contaminated andcadmium-contaminated mine site worms had elevated phytochelatin concentrations.
Ismael NA, Posma JM, Frost G, et al., 2013, The role of metabonomics as a tool for augmenting nutritional information in epidemiological studies, Electrophoresis, Vol: 34, Pages: 2776-2786
Most chronic diseases have been demonstrated to have a link to nutrition. Within food and nutritional research there is a major driver to understand the relationship between diet and disease in order to improve health of individuals. However, the lack of accurate dietary intake assessment in free-living populations, makes accurate estimation of how diet is associated with disease risk difficult. Thus, there is a pressing need to find solutions to the inaccuracy of dietary reporting.Metabolic profiling of urine or plasma can provide an unbiased approach to characterizing dietary intake and various high throughput analytical platforms have been used in order to implement targeted and non-targeted assays in nutritional clinical trials and nutritional epidemiology studies.This review describes firstly the challenges presented in interpreting the relationship between diet and health within individual and epidemiological frameworks. Secondly we aim to explore how metabonomics can benefict different types of nutritional studies and discuss the critical importance of selecting appropriate analytical techniques in these studies. Thirdly we propose a strategy capable of providing accurate assessment of food intake within an epidemioligical framework in order establish accurate associations between diet and health.
Posma JM, Garcia-Perez I, De Iorio M, et al., 2012, Subset Optimization by Reference Matching (STORM): An optimized statistical approach for recovery of metabolic biomarker structural information from ¹H NMR spectra of biofluids, Analytical Chemistry, Vol: 84, Pages: 10694-10701, ISSN: 0003-2700
We describe a new multivariate statistical approach to recover metabolite structure information from multiple 1H NMR spectra in population sample sets. SubseT Optimization by Reference Matching (STORM) was developed to select subsets of 1H NMR spectra that contain specific spectroscopic signatures of biomarkers differentiating between different human populations. STORM aims to improve the visualization of structural correlations in spectroscopic data using these reduced spectral subsets containing smaller numbers of samples than the number of variables (n<<p). We have used ‘statistical shrinkage’ to limit the number of false positive associations and to simplify the overall interpretation of the auto-correlation matrix. The STORM approach has been applied to findings from an on-going human Metabolome-Wide Association study on Body Mass Index to identify a biomarker metabolite present in a subset of the population. Moreover, we have shown how STORM improves the visualization of more abundant NMR peaks compared to a previously published method (STOCSY). STORM is a useful new tool for biomarker discovery in the ‘omic’ sciences that has a widespread applicability. It can be applied to any type of data, provided that there is interpretable correlation among variables, and can also be applied to data with more than 1 dimension (e.g. 2D-NMR spectra).
Ernst A, Ma D, Garcia-Perez I, et al., 2012, Molecular Validation of the Acute Phencyclidine Rat Model for Schizophrenia: Identification of Translational Changes in Energy Metabolism and Neurotransmission, JOURNAL OF PROTEOME RESEARCH, Vol: 11, Pages: 3704-3714, ISSN: 1535-3893
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- Citations: 27
Garcia-Perez I, Villaseñor A, Wijeyesekera A, et al., 2012, Urinary metabolic phenotyping the slc26a6 (chloride-oxalate exchanger) null mouse model, Journal of Proteome Research, Vol: 11, Pages: 4425-4435, ISSN: 1535-3893
The prevalence of renal stone disease is increasing, although it remains higher in men than in women when matched for age. While still somewhat controversial, several studies have reported an association between renal stone disease and hypertension, but this may be confounded by a shared link with obesity. However, independent of obesity, hyperoxaluria has been shown to be associated with hypertension in stone-formers and the most common type of renal stone is composed of calcium oxalate. The chloride-oxalate exchanger slc26a6 (also known as CFEX or PAT-1), located in the renal proximal tubule, was originally thought to have an important role in sodium homeostasis and thereby blood pressure control, but it has recently been shown to have a key function in oxalate balance by mediating oxalate secretion in the gut. We have applied two orthogonal analytical platforms (NMR spectroscopy and capillary-electrophoresis with UV detection) in parallel to characterize the urinary metabolic signatures related to the loss of the renal chloride-oxalate exchanger in slc26a6 null mice. Clear metabolic differentiation between the urinary profiles of the slc26a6 null and the wild type mice were observed using both methods, with the combination of NMR and CE-UV providing extensive coverage of the urinary metabolome. Key discriminating metabolites included oxalate, m-hydroxyphenylpropionylsulfate (m-HPPS), trimethylamine-N-oxide, glycolate and scyllo-inositol (higher in CFEX null mice) and hippurate, taurine, trimethylamine, and citrate (lower in slc26a6 null mice). In addition to the reduced efficiency of anion transport, several of these metabolites (hippurate, m-HPPS, methylamines) reflect alteration in gut microbial co-metabolic activities. Gender-related metabotypes were also observed in both wild type and slc26a6 null groups. Other urinary chemicals that showed a gender-specific pattern included trimethylamine, trimethylamine-N-oxide, citrate, spermidine, guanidinoacetate, and 2-
Alves AC, Li JV, Garcia-Perez I, et al., 2012, Characterization of data analysis methods for information recovery from metabolic 1H NMR spectra using artificial complex mixtures, Metabolomics, Vol: 8, Pages: 1170-1180
Yap IKS, Brown IJ, Chan Q, et al., 2010, Metabolome-Wide Association Study Identifies Multiple Biomarkers that Discriminate North and South Chinese Populations at Differing Risks of Cardiovascular Disease INTERMAP Study, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 6647-6654, ISSN: 1535-3893
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- Citations: 94
Garcia-Perez I, Angulo S, Utzinger J, et al., 2010, Chemometric and biological validation of a capillary electrophoresis metabolomic experiment of <i>Schistosoma mansoni</i> infection in mice, ELECTROPHORESIS, Vol: 31, Pages: 2338-2348, ISSN: 0173-0835
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- Citations: 13
Garcia-Perez I, Earll ME, Angulo S, et al., 2010, Chemometric analysis of urine fingerprints acquired by liquid chromatography-mass spectrometry and capillary electrophoresis: application to the schistosomiasis mouse model
Urine fingerprints from Schistosoma mansoni infected and control animals were acquired with ultra performance liquid chromatography-MS (UPLC-MS) and compared with the urine fingerprints obtained by CE by applying the same set of multivariate analysis tools. Principal component analysis of the aligned data provided a time trajectory where the infection was observed after 30 days with UPLC-MS and CE. Two main markers describing infected and control, respectively - phenyl acetyl glycine (PAG) and hippurate - were selected to illustrate the use of orthogonal partial least-square discriminant analysis in determining the discriminatory confidence. PAG was found to be significantly related to the disease (high covariance and correlation), whereas hippurate was found to be nonsignificant as an indicator. Orthogonal partial least-square discriminant analysis models were validated for sensitivity and specificity. Multivariate data analysis derived from two different detection systems showed that CE-UV and UPLC-MS found equivalent results. This work gives additional mechanistic insight into the progress of the S. mansoni infection; the biochemical role and specificity of PAG as a biomarker is yet to be determined.
Angulo S, Garcia-Perez I, Legido-Quigley C, et al., 2009, The autocorrelation matrix probing biochemical relationships after metabolic fingerprinting with CE, Electrophoresis
Fingerprinting together with statistical analysis is often employed to compare samples in metabonomic studies of a disease. Correlation algorithms can aid by extracting information based on the variation patterns of key metabolites. This information can be linked to metabolite identification or to specific up/down-regulated biochemical pathways. Matlab-based software employing the Pearson's correlation algorithm was applied to urine electropherograms from 20 mice infected with the schistosoma parasite. The fingerprints were the sum of electropherograms analysed with normal and reverse polarity, in two different modes MEKC and CZE and with two different capillaries (uncoated and polyacrylamide coated) to provide a broad picture of the samples. Hippurate, a metabolite that was depleted in the infected group and is present in both polarities, was chosen as a test variable; it correlated with itself to a p value of <0.000. Phenylacetylglycine, a metabolite shown as over expressed in the disease, was positively correlated to three metabolites in its same pathway with a correlation coefficient of 0.7 and p<0.000 to phenylalanine, 0.7 and p<0.000 to 2-hydroxyphenylacetic and 0.55 and p<0.003 to phenylacetate. The study shows that the autocorrelation matrix is able to provide extra information from data files acquired by CE analyses. It underlined an up-regulated metabolic path by association in the schistosoma infection model.
Garcia-Perez I, Couto Alves A, Angulo S, et al., 2009, Bidirectional correlation of NMR and capillary electrophoresis fingerprints: a new approach to investigating Schistosoma mansoni infection in a mouse model, Analytical chemistry, Vol: 82, Pages: 203-210
Garcia-Perez I, Vallejo M, Garcia A, et al., 2008, Metabolic fingerprinting with capillary electrophoresis, Journal of Chromatography A
Increasingly biomedical studies require a top-down approach that can be achieved by comparing patterns, signatures or "fingerprints" of metabolites that change in response to disease, toxin exposure, environmental or genetic alterations. Capillary electrophoresis is a technique well suited for the analysis of biofluids and extracted tissue. The experimental design requires a multidisciplinary team comprising chemists, informaticians, medics, etc. Here we have reviewed the field of CE fingerprinting and organised the manuscript in four main blocks, Sample treatment is a discussion of the latest methods for extraction of compounds, Analytical methods, deals with the different versions of electrophoretic methods and detection instrumentation, Chemometrics and CE fingerprinting, explains algorithms that have been presented for peak alignment, normalization, data analysis and metabolite identification, and the Applications heading focuses in urine, plasma, organic matter and plant extract studies.
Papaspyridonos K, Garcia-Perez I, Angulo S, et al., 2008, Fingerprinting of human bile during liver transplantation by capillary electrophoresis, JOURNAL OF SEPARATION SCIENCE, Vol: 31, Pages: 3058-3064, ISSN: 1615-9306
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- Citations: 13
Garcia-Perez I, Whitfield P, Bartlett A, et al., 2008, Metabolic fingerprinting of Schistosomamansoni infection in mice urine withcapillary electrophoresis, Electrophoresis, Vol: 29, Pages: 3201-3206
Schistosoma mansoni infection in mice has been fingerprinted using CE to study the capabilitiesof this technique as a diagnostic tool for this parasitic disease. Two modes ofseparation were used in generating the electrophoretic data, with each untreated urinesample the following methods were applied: (i) a fused-silica capillary, operating with anapplied potential of 18 kV, in micellar EKC (MEKC) and (ii) a polyacrylamide-coated capillary,operating with an applied potential of 220 kV under zonal CZE conditions. By combiningnormal and reverse polarities in the data treatment we have extracted more informationfrom the samples, which is a better approach for CE metabolomics. The traditionalproblems associated with variability in electrophoretic peak migration times for analyteswere countered by using a dynamic programming algorithm for the electropherogramsalignment. Principal component analyses of these aligned electropherograms and partialleast square discriminant analysis (PLS-DA) data are shown to provide a valuable means ofrapid and sample classification. This approach may become an important tool for theidentification of biomarkers, diagnosis and disease surveillance.
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