124 research outputs found
Metabolic phenotyping for enhanced mechanistic stratification of chronic hepatitis C-induced liver fibrosis
No full textOBJECTIVES: The invasive nature of biopsy alongside issues with categorical staging and sampling error has driven research into noninvasive biomarkers for the assessment of liver fibrosis in order to stratify and personalize treatment of patients with liver disease. Here, we sought to determine whether a metabonomic approach could be used to identify signatures reflective of the dynamic, pathological metabolic perturbations associated with fibrosis in chronic hepatitis C (CHC) patients.METHODS: Plasma nuclear magnetic resonance (NMR) spectral profiles were generated for two independent cohorts of CHC patients and healthy controls (n=50 original and n=63 validation). Spectral data were analyzed and significant discriminant biomarkers associated with fibrosis (as graded by enhanced liver fibrosis (ELF) and METAVIR scores) identified using orthogonal projection to latent structures (O-PLS).RESULTS: Increased severity of fibrosis was associated with higher tyrosine, phenylalanine, methionine, citrate and, very-low-density lipoprotein (vLDL) and lower creatine, low-density lipoprotein (LDL), phosphatidylcholine, and N-Acetyl-α1-acid-glycoprotein. Although area under the receiver operator characteristic curve analysis revealed a high predictive performance for classification based on METAVIR-derived models, <40% of identified biomarkers were validated in the second cohort. In the ELF-derived models, however, over 80% of the biomarkers were validated.CONCLUSIONS: Our findings suggest that modeling against a continuous ELF-derived score of fibrosis provides a more robust assessment of the metabolic changes associated with fibrosis than modeling against the categorical METAVIR score. Plasma metabolic phenotypes reflective of CHC-induced fibrosis primarily define alterations in amino-acid and lipid metabolism, and hence identify mechanistically relevant pathways for further investigation as therapeutic targets.</p
NMR-based metabonomics of pre-clinical models of isoniazid and gentamicin toxicity
No full textAn individual's response to drug therapy is the result of complex interactions between environmental and genetic factors. The ability to characterise inter-individual variation in response to pharmaceutical intervention is of particular importance in a clinical setting, where variation in response can result in therapeutic failure or adverse effects in individuals or sub-populations of patients. Given the success of NMR-based techniques for metabolic profiling, this thesis focused on characterisation of the systems-wide endogenous metabolic response to isoniazid and gentamicin. Throughout this work, one-dimensional 1H and two-dimensional 1H NMR of biofluids and tissue was applied to generate metabolic profiles reflective of response to pharmaceutical intervention in the animal model, in conjunction with conventional clinical chemistry and histopathological assessment.
Isoniazid is a widely prescribed anti-tubercular treatment that has toxic side effects: it is widely associated with both hepatotoxicity and peripheral neurotoxicity in the clinical setting. Here, a relationship was established between the post dose profile of drug metabolites and the severity of the adverse effect on the central nervous system. Further, the metabonomic approach resulted in the identification of pre-dose urinary markers of toxic outcome.
The aminoglycoside antibiotic gentamicin is a known nephrotoxin. Clinical chemistry and histopathology identified clear differences in the degree of nephrotoxicity experienced by the rat relative to administration time. Complementary metabolic profiling techniques were then applied to the analysis of urine and kidney tissue, and identified clear metabolic differences in response to treatment time. A final study then explored whether co-administration of a statin (atorvastatin) could reduce gentamicin-induced nephrotoxicity in the rat. Conventional toxicity assessments indicated that co-administration of gentamicin and atorvastatin was non-toxic, while metabonomic analysis of urine and kidney tissue indicated that there were metabolic differences, likely linked to the antibiotic effect of gentamicin. This work highlights the potential for beneficial drug-drug interactions to decrease a drugs toxic effect, as well as the importance of considering administration time when developing treatment regimens
Targeted and untargeted liquid chromatography-mass spectrometry (LC-MS) metabolic profiling in population cohort studies
No full textIncorporating liquid chromatography-mass spectrometry (LC-MS) based metabolite profiling into molecular epidemiological studies holds the potential to identify biomarkers that link genetics, environmental exposures (the exposome) and phenotypes to multifactorial diseases. This thesis applied and examined three LC-MS platforms for three different kinds of studies in the context of exposome research.
A targeted, fully quantitative LC-MS/MS method was applied to quantify 14 urinary endogenous oestrogen metabolites; the results demonstrated identification of metabolic phenotype for the genetic polymorphism in CYP3A gene associated with breast cancer in pre-menopausal women. A combined targeted LC-MS/MS and direct infusion-MS/MS method were used to explore the metabolic response to prenatal exposures to persistent organic pollutants (POPs) in 1st trimester maternal and cord serum. The results showed an association of POPs with concentration variation in citrulline and diacyl phosphatidylcholines in both maternal and cord serum and kynurenine/tryptophan ratio in mothers indicating altered indoleamine 2, 3-dioxygenase enzyme activity. Finally, an untargeted LC-MS metabolite profiling pilot study was conducted to extend the limited knowledge of the urinary metabolites level temporal variability in healthy children. Despite high precision, the data demonstrated large inter- and intra-individual variation in metabolite levels.
Through these studies, the broader challenges for the successful application of LC-MS metabolome analysis in identifying molecular biomarkers from population studies are explored. These specifically include data comparability, lack of longitudinal variability data and pre-analytical biases. In summary, this thesis illustrates the value of LC-MS to exposome research and identifies the strengths and weakness of different experimental strategies used to characterise metabolic phenotypes in population studies.Open Acces
Statistical correlation based methods for enhanced interpretation of and information recovery from NMR metabolic data sets
No full textOwing to its ability to capture a systemic and temporal metabolic description of an
organism’s response to a treatment, metabonomics is a well-established and valuable
approach in elucidating the effects and mechanisms of a given perturbation. However,
to optimise information recovery from the complex datasets generated, chemometric
methods are essential.
The work presented in this thesis focuses on the development of novel
methods, and the use of existing methods in new applications to ease data
interpretation and enhance information recovery from 1H Nuclear Magnetic
Resonance (NMR) metabonomic datasets using correlation based methods. Although
the methods here are largely applied to toxicological data, they could be equally
valuable in the analysis of any metabonomic dataset, and indeed potentially to other
‘omics’ data presenting similar analytical challenges.
The first two methodological approaches relate to novel extensions of
Statistical Total Correlation Spectroscopy (STOCSY), a valuable tool in elucidation
of both inter- and intra-metabolite spectral intensity correlations in NMR
metabonomic datasets. In the first, STOCSY is utilised in STOCSY-editing, a method
for the selective identification and downscaling of the peaks from unwanted
metabolites such as those arising from xenobiotics. Structurally correlated peaks from
drug metabolites are first identified using STOCSY, and the returned correlation
information utilised to scale the spectra across these regions, producing a modified set
of spectra in which drug metabolite contributions are reduced, endogenous peaks
reconstructed and thus, analysis by pattern recognition methods without drug
metabolite interferences facilitated. In the second, the STOCSY approach is extended
in Iterative-STOCSY, where metabolic associations are followed over several rounds
of STOCSY through calculation of correlation coefficients initially from a driver
spectral peak of interest, and subsequently from all peaks identified as correlating
above a set threshold to peaks picked in the previous round. The condensation of
putatively structurally related peaks into single nodes, and representation of the
otherwise complex network in a fully interactive plot of node-to-node connections
and corresponding spectral data, allows the ready exploration of both inter- and intrametabolite
relationships and a more directed approach to the identification of
biomarkers of the studied perturbation. Finally various clustering methods are investigated with the aim of providing improved structural (intra-metabolite) versus
non-structural (inter-metabolite) assignment. Thus, this thesis presents a framework
for the enhanced identification, recovery and characterisation of inter- and intrametabolite
relationships and how these are affected by metabonomic perturbation
Metabolic profiling and population screening of analgesic usage in nuclear magnetic resonance spectroscopy-based large-scale epidemiologic studies
No full textThe application of a 1H nuclear magnetic resonance (NMR) spectroscopy-based screening method for determining the use of two widely available analgesics (acetaminophen and ibuprofen) in epidemiologic studies has been investigated. We used samples and data from the cross-sectional INTERMAP Study involving participants from Japan (n = 1145), China (n = 839), U.K. (n = 501), and the U.S. (n = 2195). An orthogonal projection to latent structures discriminant analysis (OPLS-DA) algorithm with an incorporated Monte Carlo resampling function was applied to the NMR data set to determine which spectra contained analgesic metabolites. OPLS-DA preprocessing parameters (normalization, bin width, scaling, and input parameters) were assessed systematically to identify an optimal acetaminophen prediction model. Subsets of INTERMAP spectra were examined to verify and validate the presence/absence of acetaminophen/ibuprofen based on known chemical shift and coupling patterns. The optimized and validated acetaminophen model correctly predicted 98.2%, and the ibuprofen model correctly predicted 99.0% of the urine specimens containing these drug metabolites. The acetaminophen and ibuprofen models were subsequently used to predict the presence/absence of these drug metabolites for the remaining INTERMAP specimens. The acetaminophen model identified 415 out of 8436 spectra as containing acetaminophen metabolite signals while the ibuprofen model identified 245 out of 8604 spectra as containing ibuprofen metabolite signals from the global data set after excluding samples used to construct the prediction models. The NMR-based metabolic screening strategy provides a new objective approach for evaluation of self-reported medication data and is extendable to other aspects of population xenometabolome profiling
Metabonomic and epidemiological analyses of maternal parameters and exposures during pregnancy and their influence on fetal growth amongst the INMA birth cohort
No full textFetal growth aberrations, including fetal growth restriction (FGR) and macrosomia, convey the highest risk of perinatal mortality and morbidity, as well as increasing the chance of developing chronic disease in later life. Using Metabolic profiling/metabolomics approaches in maternal urine samples collected in a prospective mother-child cohort can provide information on the early-life exposome and can be linked to child health outcomes as well as potentially identify new biomarkers of exposure. The aims of this PhD were to characterise intra and inter-individual variations in maternal urine profiles during pregnancy, predict fetal growth outcomes and identify environmental sources of metabolic variations.
We applied an exploratory metabolic profiling approach using 1H nuclear magnetic resonance (NMR) spectroscopy to maternal urine samples at the first (n=806) and third trimesters of gestation (n=886), collected as part of the Infancia y Medio Ambiente (INMA)–Environment and Childhood Study, a large prospective mother-child population-based cohort study cohorts based in eight Spanish cities. An exploratory metabolomics approach was applied using 1H nuclear magnetic resonance (NMR) spectroscopy for profiling and LC-MS/MS for metabolite identification. Metabolites were used to predict longitudinal measures of fetal growth in terms of body weight and head size (estimated at 12th, 20th and 34th gestational weeks and at birth) and placental weight at birth using linear regression adjusting for main confounding factors.
To our knowledge the present study represents the largest human investigation (n >800) in which non-targeted proton nuclear magnetic resonance spectroscopy has been used to understand the progression of normal fetal growth in two different Spanish populations. We identified 10 reproducible metabolic associations at week 34 with estimated fetal weight, birth weight and placental weight. These signatures included pregnancy-related hormone breakdown products that were newly characterised in our study and branched-amino acids (BCAAs) isoleucine, valine and leucine with its catabolic intermediate 3-hyrdoxyisobutyrate. Overall metabolic phenotypes at week 12 could not predict fetal weight at week 34 or at birth, but only at weeks 12 and 20 and with little consistency across the two populations. Unique adverse metabolic signatures at week 12 of fetal growth were found in Sabadell related to mitochondrial oxidative stress, systemic inflammation and renal function. These findings captured the metabolic signatures of a myriad of physiological (both maternal and fetal), environmental, and other lifestyle characteristics associated with fetal growth. Sensitive measures of environmental exposure to HAA toxins were also created using LC-MS, a non-volatile sub-type of water contaminants, using gold-standard urine biomarker (TCAA), in a case-control study for use in future epidemiological studies of fetal growth outcomes.
This work provides ground breaking evidence of clinical relevance with the potential to personalise pre-natal care and ensure healthy fetal development.Open Acces
Metabolic phenotyping applied to pre-clinical drug induced liver injury and acute liver failure
No full textLiver disease is a prevalent clinical challenge caused by a variety of factors including viral infections and xenobiotic overdose. Improving our mechanistic understanding of disease development will lead to an improved stratification of patients and ultimately a better prognosis. This thesis addresses the role of metabonomics in providing insight into a variety of preclinical models and a clinical study. The metabolic phenotype of preclinical models of paracetamol (APAP) and methotrexate (MTX) toxicity were characterised, together with the study of clinical samples from decompensated cirrhosis, acute on chronic liver failure (ACLF) and acute liver failure (ALF) patients. A comparative metabonomic approach was applied to study the metabolic consequences following administration of APAP and its non-hepatotoxic isomer; N-acetyl-m-aminophenol (AMAP), in mice. The analysis revealed an APAP-induced hepatotoxicity through mitochondrial dysfunction which was characterized by an upregulation of glycolysis as well as the inhibition of beta-oxidation and the Krebs cycle. The metabolic and toxic effects of MTX were investigated in healthy rats and in a model of non-alcoholic steatohepatitis (NASH; as modelled by the methionine choline deficient diet). MTX was shown to have an enhanced toxicity in the context of NASH, which was associated with unique metabolic changes. The clinical work revealed that the serum metabolic phenotypes of clinical decompensated cirrhosis, ACLF and ALF patients were also each characterized by unique metabolic perturbations. The ALF phenotype was associated with the most metabolic changes and consisted of markers of oxidative and energetic stress, as well as markers of amino acid metabolism dysfunction. Subsequently, the novel serum metabolic profiling of the hepatic vein, hepatic portal vein and peripheral artery of patients during liver transplantation aimed to characterize the hepatic disease microenvironment, which was discovered to mainly consist of a perturbed amino acid metabolism.Open Acces
Faculty Opinions recommendation of Utility of spherical human liver microtissues for prediction of clinical drug-induced liver injury.
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