1,721,176 research outputs found
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Hippurate: the natural history of a mammalian-microbial co-metabolite
No full textHippurate, the glycine conjugate of benzoic acid, is a normal constituent of the endogenous urinary metabolite profile and has long been associated with the microbial degradation of certain dietary components, hepatic function and toluene exposure, and is also commonly used as a measure of renal clearance. Here we discuss the potential relevance of hippurate excretion with regards to normal endogenous metabolism and trends in excretion relating to gender, age, and the intestinal microbiota. Additionally, the significance of hippurate excretion with regards to disease states including obesity, diabetes, gastrointestinal diseases, impaired renal function, psychological disorders and autism, as well as toxicity and parasitic infection, are considered
Optimization and Evaluation of Metabolite Extraction Protocols for Untargeted Metabolic Profiling of Liver Samples by UPLC-MS
No full textA series of six protocols were evaluated for UPLC-MS based untargeted metabolic profiling of liver extracts in terms of reproducibility and number of metabolite features obtained. These protocols, designed to extract both polar and nonpolar metabolites, were based on (i) a two stage extraction approach or (ii) a simultaneous extraction in a biphasic mixture, employing different volumes and combinations of extraction and resuspension solvents. A multivariate statistical strategy was developed to allow comparison of the multidimensional variation between the methods. The optimal protocol for profiling both polar and nonpolar metabolites was found to be an aqueous extraction with methanol/water followed by an organic extraction with dichloromethane/methanol, with resuspension of the dried extracts in methanol/water before UPLC-MS analysis. This protocol resulted in a median CV of feature intensities among experimental replicates of <20% for aqueous extracts and <30% for organic extracts. These data demonstrate the robustness of the proposed protocol for extracting metabolites from liver samples and make it well suited for untargeted liver profiling in studies exploring xenobiotic hepatotoxicity and clinical investigations of liver disease. The generic nature of this protocol facilitates its application to other tissues, for example, brain or lung, enhancing its utility in clinical and toxicological studies.<br/
Metabolic trajectory characterisation of xenobiotic-induced hepatotoxic lesions using statistical batch processing of NMR data : Nicholson Jeremy K., Holmes Elaine
No full textMultivariate statistical batch processing (BP) analysis of 1H NMR urine spectra was employed to establish time-dependent metabolic variations in animals treated with the model hepatotoxin, -naphthylisothiocyanate (ANIT). ANIT (100 mg kg-1) was administered orally to rats (n = 5) and urine samples were collected from dosed and matching control rats at time-points up to 168 h post-dose. Urine samples were measured via1H NMR spectroscopy and partial least squares (PLS) based batch processing analysis was used to interpret the spectral data, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile over the 7 day study period was established, together with model confidence limits (±3 standard deviation), for the control group. Samples obtained from ANIT treated animals were evaluated using the control model. Time-dependent deviations from the control model were evident in all ANIT treated animals consisting of glycosuria, bile aciduria, an initial decrease in taurine levels followed by taurinuria and a reduction of tricarboxylic acid cycle intermediate excretion. BP provided an efficient means of visualising the biochemical response to ANIT in terms of both inter-animal variation and net variation in metabolite excretion profiles. BP also allowed multivariate statistical limits for normality to be established and provided a template for defining the sequence of time-dependent metabolic consequences of toxicity in NMR based metabonomic studies.</p
Obesity and Cage Environment Modulate Metabolism in the Zucker Rat: A Multiple Biological Matrix Approach to Characterizing Metabolic Phenomena
Full text linkObesity and its comorbidities are
increasing worldwide imposing
a heavy socioeconomic burden. The effects of obesity on the metabolic
profiles of tissues (liver, kidney, pancreas), urine, and the systemic
circulation were investigated in the Zucker rat model using 1H NMR spectroscopy coupled to multivariate statistical analysis.
The metabolic profiles of the obese (fa/fa) animals were clearly differentiated from the two phenotypically
lean phenotypes, ((+/+) and (fa/+)) within each biological
compartment studied, and across all matrices combined. No significant
differences were observed between the metabolic profiles of the genotypically
distinct lean strains. Obese Zucker rats were characterized by higher
relative concentrations of blood lipid species, cross-compartmental
amino acids (particularly BCAAs), urinary and liver metabolites relating
to the TCA cycle and glucose metabolism; and lower amounts of urinary
gut microbial-host cometabolites, and intermatrix metabolites associated
with creatine metabolism. Further to this, the obese Zucker rat metabotype
was defined by significant metabolic alterations relating to disruptions
in the metabolism of choline across all compartments analyzed. The
cage environment was found to have a significant effect on urinary
metabolites related to gut-microbial metabolism, with additional cage-microenvironment
trends also observed in liver, kidney, and pancreas. This study emphasizes
the value in metabotyping multiple biological matrices simultaneously
to gain a better understanding of systemic perturbations in metabolism,
and also underscores the need for control or evaluation of cage environment
when designing and interpreting data from metabonomic studies in animal
models
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Integrated cytokine and metabolic analysis of pathological responses to parasite exposure in rodents
No full textParasitic infections cause a myriad of responses in their mammalian hosts, on immune as well as on metabolic level. A multiplex panel of cytokines and metabolites derived from four parasite-rodent models, namely, Plasmodium berghei-mouse, Trypanosoma brucei brucei-mouse, Schistosoma mansoni-mouse, and Fasciola hepatica-rat were statistically coanalyzed. 1H NMR spectroscopy and multivariate statistical analysis were used to characterize the urine and plasma metabolite profiles in infected and noninfected animals. Each parasite generated a unique metabolic signature in the host. Plasma cytokine concentrations were obtained using the ‘Meso Scale Discovery’ multi cytokine assay platform. Multivariate data integration methods were subsequently used to elucidate the component of the metabolic signature which is associated with inflammation and to determine specific metabolic correlates with parasite-induced changes in plasma cytokine levels. For example, the relative levels of acetyl glycoproteins extracted from the plasma metabolite profile in the P. berghei-infected mice were statistically correlated with IFN-γ, whereas the same cytokine was anticorrelated with glucose levels. Both the metabolic and the cytokine data showed a similar spatial distribution in principal component analysis scores plots constructed for the combined murine data, with samples from all infected animals clustering according to the parasite species and whereby the protozoan infections (P. berghei and T. b. brucei) grouped separately from the helminth infection (S. mansoni). For S. mansoni, the main infection-responsive cytokines were IL-4 and IL-5, which covaried with lactate, choline, and D-3-hydroxybutyrate. This study demonstrates that the inherently differential immune response to single and multicellular parasites not only manifests in the cytokine expression, but also consequently imprints on the metabolic signature, and calls for in-depth analysis to further explore direct links between immune features and biochemical pathways
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Unsupervised analysis of mass spectral data
Full text linkOver the last few decades, mass spectrometry imaging (MSI) has gained increasing interest as an analytical tool for the analysis of spatial molecular patterns in samples of interest. Because of its untargeted and high-throughput nature, MSI data often consists of hundreds to thousand spectral peak images.
Statistical analysis of this type of data has been extensively used to investigate the relationship between the observed molecular patterns and the local properties of the sample. For example, supervised learning techniques can be employed to segment the tissue specimens into histologically relevant areas. In general, these regions must be identified by an expert histopathologist by visual inspection of the optical image of the tissue specimen; for instance, by employing haematoxylin and eosin (H&E) staining. Using this approach, lists of significantly up/down-regulated ions in the various tissue regions can be identified by univariate or multivariate statistical modelling.
Unfortunately, these methods cannot be used when there is no certainty about a direct relationship between histological characteristics and molecular patterns. This situation is typical of analyses where underlying models of the local molecular interactions are unknown, such as in cancerous tissue. In these cases, different data analysis tools must be employed.
Unsupervised data analysis provides a class of techniques and models that can identify data patterns without requiring external information common to objects of the same class. The purpose of these methods is to identify statistical properties common to subsets of the analysed data and to generate a partition based on these. In the case of MSI, unsupervised methods usually employed fall into the category of clustering. The purpose of these methods is to generate a partition of the measured spectra, accordingly to the similarity between their ion patterns, and assign these to the corresponding pixels, thus providing a molecular-based segmentation of the sample. However, the lack of a ground truth makes the clustering challenging, requiring various strategies for validating the quality of the observed results.
Furthermore, the unprocessed MSI data often contains signals which are the result of the specific analytical technique used to extract the molecular content of the sample of interest. For instance, MALDI contains signals associated with the chemical matrix used to enhance the ion desorption, or, in the case of DESI, solvent-related signals are present in the final dataset. It is evident that these sample-unrelated signals can interfere with the results of the unsupervised analysis.
For this reason, a series of filters, published in an R package, called SPatially aUTomatic deNoising for Ims toolKit (SPUTNIK), are presented. The filters aim to identify and remove spectral signals that are not likely to be sample-related. The results show that this approach not only significantly reduces the size of the data, but also improves the quality of the clustering results.
Subsequently, the benefit of dimensionality reduction (DR) techniques in determining the optimal number of clusters in large MSI data is investigated. It is shown that although standard linear methods, such as principal component analysis (PCA), cannot provide an accurate and comprehensive picture of the statistical properties of the data, deep-learning-based (highly non-linear) methods reveal the presence of groups of mutually similar spectra. Additionally, the benefit of using a 3-dimensional (3D) tissue specimen for generating robust unsupervised partitions of the data is presented.
Finally, the information contained in MSI data about the spatial localisation of the detected molecules is exploited for the identification of groups of highly co-localised ions. Using the hypothesis that groups of highly co-localised molecules can be an expression of local metabolism, differences in the ion co-localisation patterns between metastatic and non-metastatic colorectal cancer are identified.Open Acces
Time-dependent metabolic phenotyping of inflammatory dysregulation
Full text linkA rich and functional description of a patient health status is the fundamental basis for the personalisation of treatment and the targeting of interventions. The function of inflammation in the healing process as well as its involvement in most major diseases is well established, yet the specific mechanism by which it contributes to the pathogenesis is still not fully understood. If conditions arising from a dysregulation of the inflammatory process are to be treated before they become irreversible, a novel understanding of these pathologies must be achieved and a stratification of patients based on their inflammatory status undertaken.
The work presented in this thesis aims to deliver new analytical and statistical approaches to support the investigation of the time-dependent dysregulation of inflammation.
Lipid mediators have been described as exerting a major role in the initiation and regulation of the inflammatory response, yet analytical platforms for their large-scale characterisation in human biofluids are lacking. This thesis reports the validation of an assay for the simultaneous quantification of pro- and anti-inflammatory signalling molecules in multiple human biofluids. The coverage of the assay in each biofluid is subsequently established, characterising inflammatory signalling across biological compartments. A second study explores the assay’s applicability in a clinical context; investigating the relationship between lipid mediators, current clinical markers of inflammation and post-operative complications.
Characterising the interplay between signalling and regulatory networks is key to understanding a living system’s response to perturbations, yet few statistical approaches are suited for the detection of time-dependent patterns in short and irregularly sampled longitudinal datasets. This thesis reports the development of a statistical approach to support the identification of altered time-trajectories in such studies. The method’s wide applicability is subsequently demonstrated on two investigations covering the diversity of metabolic phenotyping data generation platforms.
This thesis is a proof of concept for the characterisation of patient-specific inflammatory status in a clinical context and the identification of altered time-dependent patterns. Both analytical and statistical developments have been motivated by the needs of real world applications and provide a template for the characterisation and analysis of the molecular basis for treatment.Open Acces
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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