Esquistosomiasis en raton, modelo para estudios metabolomicos
- Autores: Isabel Garcia Perez
- Directores de la Tesis: Coral Barbas Arribas (dir. tes.)
- Lectura: En la Universidad CEU San Pablo ( España ) en 2010
- Idioma: español
- Tribunal Calificador de la Tesis: María Luisa Marina Alegre (presid.), Antonia García Fernández (secret.), Alejandro Cifuentes Gallego (voc.), Carmen del Águila de la Puente (voc.), Elaine Holmes (voc.)
- Materias:
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- Resumen
The development of ever more powerful analytical techniques combined with the increasing availability of genome sequences has led to a huge interest in the application of omic technologies such as: genomics, proteomics and metabonomics.
A metabolite is a small molecule that is a product of the metabolism. They are involved in all he biological systems. Genomics deals with responses at a genomic level whilst proteomic deals with responses to proteins at a cellular level. Neither of these provide a complete description of the system as a whole. Systems biology studies the complex interactions in biological systems and how these interactions enable the functioning and specific behavior of that system (1,2).
Nevertheless, the technology involved for a global and quantitative evaluation is daunting because the physical properties of the compounds are diverse and they vary a lot in concentration. The different approaches to analyse metabolites can be grouped into five classes: i) metabolite target analysis: the classical target-driven hypothesis where one or a few single compounds are analysed; ii) metabolic profiling: pre-defined metabolites chosen based upon a class of compounds (such as aminoacids, organic phosphates, fatty acids or carbohydrates), or based upon their association with a specific pathway; iii) metabolic fingerprinting: global high-throughput, rapid analysis aiming sample classification through pattern recognition; iv) metabolomics: the non biased identification and quantification of all the metabolites in a biological system (3, 4). v) metabonomics has been defined by Nicholson et al such as: The quantitative measurement of time-related multi-parametric metabolic responses of a living system to pathophysiological stimuli or genetic modification (5). Confusion arises as definitions can be inclusive or historically favoured by the scientific group performing the study. Despite the differences in definitions of metabonomics and metabolomics, the two approaches are very closely related, sharing the idea that differences between samples can be detected and studied by means of their metabolic composition. Moreover, metabolomic is expected to provide more accurate and meaningful information about biological systems compared to the information provided so far by genomics, proteomics and transcriptomics (6).
Metabolomics has been acquired status as an efficient analytical tool for the study of the metabolic consequences in many different areas of biological and clinical science, such as: toxicology (7-9), clinical diagnosis, nutritional biochemistry (10-13), parasitology (14, 15), fisiology (16), etc Metabolite target analysis and metabolic profiling are already classical biochemical tools.
This thesis will be devoted mainly to metabolic fingerprinting with capillary electrophoresis (CE) and Ultra Performance Liquid Chromatography- Mass Spectrometry (UPLC-MS) and their application to S. mansoni infection. Metabolic fingerprinting, is a non-target methodology where, after getting a snapshot as comprehensive as possible, all detectable peaks (or signals), including unknowns, are considered to establish sample classification. Initially in this approach the intention is not to identify each observed metabolite, but to compare patterns, signatures or ingerprints of metabolites that change in response to disease, toxin exposure, environmental or genetic alterations.
However, to identify and interpret the most important biological effects, the identification of the unknowns which are key to the classification is necessary.
Schistosomiasis is one of the most burdensome of the neglected diseases, with 200 million people infected and 400 million people are at risk of infection (17, 18). The parasites that are responsible for the disease are the schistosomes.
They are trematode worms that live in the bloodstream of animals and humans. Infection is widespread with a relatively low mortality rate, but high morbidity rate, causing severe debilitating illness in millions of people. Early diagnosis and prompt treatment of schistosomal infection are thus crucial.
Wang et al. pioneered the application of metabolic fingerprinting in parasitology (15). A study was realized in which mice were infected with 80 S.mansoni cercariae each. Samples were analysed with 1HNMR spectroscopy and multivariate pattern recognition.
The results of the study found that the metabolomic characterisitic of the S.mansoni infection has reduced leves of tricarboxylic acid cycle intermediates, including citrate, succinate, and 2-oxoglutarate and increased levels of pyruvate. Moreover the depletion of taurine, 2-oxoisocaproate and 2-oxoisovalerate and elevation of tryptophan in the urine can be associated to a disturbance of amino acid metabolism and metabolites related to the gut microbiota disturbance were also identified.
Another similar study was performed by the same team (19). The objective was to investigate the metabolomic responses of Syrian hamsters to S.japonicum infection using 1HNMR and pattern recognition. To this effect male hamster where infected with 100 S. japonicum each. The mayor difference between the 2 studies was that in a S. japonicum infection the hamster showed an inhibition of short-chain fatty acids.
The metabolomic experiment follows a pipeline that goes from the careful design of experiments, through adequate sample treatment, instrumental selection and optimization, data storage and manipulation, followed by chemometric data processing methods, and results validation and crossvalidation.
Hence conclusions are most likely to be robust when applied in comparable circumstances to samples not used in their obtaining (20).
The aspects specifically related to CE fingerprinting will be discussed in the First Chapter of this thesis. Schistosoma mansoni infection in mice has been fingerprinted using CE to study the capabilities of this technique as a diagnostic tool for this parasitic disease.
Two modes of separation were used in generating the electrophoretic data.
With each untreated urine sample the following methods were applied: (i) a fused-silica capillary, operating with an applied potential of 20 kV, in micellar Electrokinetic Chromatography (MEKC) and a polyacrylamide-coated capillary, operating with an applied potential of 25 kV in Capillary Zonal Electrophoresis (CZE) conditions. By combining normal and reverse polarities in the data treatment we have extracted more information from the samples, which is a better approach for CE metabolomics. The traditional problems associated with variability in electrophoretic peak migration times for analytes, were countered by using a dynamic programming algorithm for the electropherograms alignment.
Principal components analyses of these aligned electropherograms and partial least square discriminant analysis (PLS-DA) data are shown to provide a valuable means of rapid and sample classification. This approach may become an important tool for the identification of biomarkers, diagnosis and disease surveillance. The aim of this chapter was to explore the application of these CE methods, in combination, for profiling S. mansoni infections in mice and, through data alignment and multivariate pattern recognition techniques, to provide a tool for both diagnosis and disease surveillance.
In the Second Chapter, validation for models obtained by CE fingerprinting of urine from mice infected with Schistosoma mansoni was proposed. Samples from two sets of mice infected were analysed. This study is an inter-laboratory experiment where different infection methods and animal husbandry procedures were employed in order to establish the core biological response to this parasite. CE data was analysed using principal components analysis, partial least square and orthogonal partial least square analysis. Validation of the scores consisted of permutation scrambling (100 repetitions) and a manual validation method, using 1/3 of the samples (not included in the model) as a test or prediction set. In this inter-laboratory study the validation yield 100% selectivity and 100% sensitivity. This demonstrates the robustness of these models with respect to deciphering metabolic perturbations in Schistosoma infection in the mouse model.
Moreover an identification study based on the spiking of the samples and comparing the migration time and spectra of the pure standard, has been explored in order to identify as many peaks in the CE profile as possible. In addition, it was hoped that the putative metabolomic changes induced by infection might, through biomarker identification, enhance our understanding of the biochemical interactions between host and parasite in this mouse model.
On the other hand, 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. For this reason, in the Third Chapter 3, 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. This new multivariate graphical statistical approach, based on the novel combination of projection on latent structure discriminant anlysis (PLS-DA) of the capillary electrophoresis profiles, coupled with the autocorrelation matrix (ACM) study will be used to characterize the in vivo metabolic pathway perturbations of a model of S.mansoni infection in mice.
Essentially, two analytical probes have been used for metabolomic applications, chromatographic and spectroscopic techniques e.g. HPLC, GC, CE, hyphenated techniques, e.g. GC-MS, LC-MS, CE-MS and spectroscopic techniques e.g. NMR, FT-IR and FT-ICR-MS and they have been comparatively revised elsewhere . The choice of separation/resolution method can be difficult. Ideally it should be sensitive, selective and matrix independent. This is not always possible especially when each technique has its limitations, but more and more studies are now performed with an analytical platform comprising several techniques.
According to this, the aim of the Fourth Chapter , is to understand the onset and progression of S. mansoni infection by combining CE and UPLC-MS as tools for metabonomic studies.
We have used UPLC-MS to provide a urine signature of ten infected mice over a period of 57 days and the equivalent number of control animals during the same time span. The urine samples were injected with no sample pretreatment and a 12 minute gradient run on a C-18 column. We have also used CE for the analysis of these same samples. By combining two polarities we obtained a single extended electropherogram for urinary profiles of the infected and control animals.
Urine fingerprints acquired with UPLC-MS were statistically compared with the same urine fingerprints obtained by CE analysis. Principal component analysis (PCA) of the aligned data provided a time trajectory where the infection was observed after thirty days with both techniques.
The relevance of variables in the loading plots has also been studied and often confirmed by utilising S-plots in OPLS-DA models. Furthermore, inter-laboratory study was proposed in the Fifth Chapter 5. This time, samples from two sets of mice infected by Schistosomiasis, were analysed using two different UPLC-MS instruments but same analytical conditions were applied. Animal experiments were performed in different countries, on different breeds of mice, with different methods of infection. Validation of the chemometric models consisted of permutation scrambling (100 repetitions) and an external manual validation method were required in orther to confirm the robustness of the models and the usefulness of the technique. However, comparation of the two sets of samples biological response to the infection will be the final aim of this chapter.
Finally, we will demonstrate in Sixth Chapter 6, that the statistical integration of nuclear magnetic resonance (NMR) spectroscopy and capillary electrophoresis (CE) data in order to describe a pathological state caused by Schistosoma mansoni infection in a mouse model based on urinary metabolite profiles. Urine samples from mice 53 days post infection with S. mansoni and matched controls were analyzed via NMR spectroscopy and CE. The two sets of metabolic profiles were first processed and analyzed independently and were subsequently integrated using statistical correlation methods in order to facilitate cross assignment of metabolites.
Using this approach, metabolites such as 3-ureidopropionate, p-cresol glucuronide, phenylacetylglycine, indoxyl sulfate, isocitrate, and trimethylamine were identified as differentiating between infected and control animals. These correlation analysis facilitated structural elucidation using the identification power of one technique to enhance and validate the other, but also highlighted the enhanced ability to detect functional correlations between metabolites, thereby providing potential for achieving deeper mechanistic insight into the biological process.
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