| 1. |
- Boles, Usama, et al.
(author)
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Dysregulated fatty acid metabolism in coronary ectasia : An extended lipidomic analysis
- 2017
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In: International Journal of Cardiology. - : Elsevier BV. - 0167-5273 .- 1874-1754. ; 228, s. 303-308
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Journal article (peer-reviewed)abstract
- BACKGROUND: Coronary artery ectasia (CAE) is not an uncommon clinical condition, which could be associated with adverse outcome. The exact pathophysiology of the disease is poorly understood and is commonly interpreted as a variant of atherosclerosis. In this study, we sought to undertake lipidomic profiling of a group of CAE patients in an attempt to achieve better understanding of its disturbed metabolism.METHODS: Untargeted lipid profiling and complementary modelling strategies were employed to compare serum samples from 16 patients with CAE (mean age 63.5±10.1years, 6 female) and 26 controls with normal smooth coronary arteries (mean age 59.2±6.6years and 7 female). Sample preparation, LC-MS analysis and metabolite identification were performed at the Swedish Metabolomics Centre, Umeå, Sweden.RESULTS: Phosphatidylcholine levels were significantly distorted in the CAE patients (p=0.001-0.04). Specifically, 16-carbon fatty acyl chain phosphatidylcholines (PC) were detected in lower levels. Similarly, 11 meioties of Sphyngomyelin (SM) species were detected at lower concentrations (p=0.000001-0.01) in the same group. However, only three metabolites were significantly higher in the pure CAE subgroup (6 patients) when compared with the 10 mixed CAE patients (two meioties of SM species and one of PC). Atherosclerosis risk factors were not different between groups.CONCLUSION: This is the first lipid profiling study reported in coronary artery ectasia. While the lower concentration and dysregulation of sphyngomyelin suggests an evidence for premature apoptosis, that of phosphatidylcholines suggests perturbed fatty acid elongation/desaturation, thus may be indicative of non-atherogenic process in CAE.
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| 2. |
- Bouveresse, D. Jouan-Rimbaud, et al.
(author)
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Identification of significant factors by an extension of ANOVA-PCA based on multi-block analysis
- 2011
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 106:2, s. 173-182
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Journal article (peer-reviewed)abstract
- A modification of the ANOVA-PCA method, proposed by Harrington et al. to identify significant factors and interactions in an experimental design, is presented in this article. The modified method uses the idea of multiple table analysis, and looks for the common dimensions underlying the different data tables, or data blocks, generated by the "ANOVA-step" of the ANOVA-PCA method, in order to identify the significant factors. In this paper, the "Common Component and Specific Weights Analysis" method is used to analyse the calculated multi-block data set. This new method, called AComDim, was compared to the standard ANOVA-PCA method, by analysing four real data sets. Parameters computed during the AComDim procedure enable the computation of F-values to check whether the variability of each original data block is significantly greater than that of the noise.
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| 3. |
- Climaco Pinto, Rui, et al.
(author)
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Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets.
- 2022
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In: Analytical chemistry. - : American Chemical Society (ACS). - 1520-6882 .- 0003-2700. ; 94:14, s. 5493-5503
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Journal article (peer-reviewed)abstract
- Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
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| 4. |
- Pinto, Rui Climaco, et al.
(author)
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Advantages of orthogonal inspection in chemometrics
- 2012
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In: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 26:6, s. 231-235
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Journal article (peer-reviewed)abstract
- The demand for chemometrics tools and concepts to study complex problems in modern biology and medicine has prompted chemometricians to shift their focus away from a traditional emphasis on model predictive capacity toward optimizing information exchange via model interpretation for biological validation. The interpretation of projection-based latent variable models is not straightforward because of its confounding of different systematic variations in the model components. Over the last 15?years, this has spurred the development of orthogonal-based methods that are capable of separating the correlated variation (to Y) from the noncorrelated (orthogonal to Y) variations in a single model. Here, we aim to provide a conceptual explanation of the advantages of orthogonal variation inspection in the context of Partial Least Squares (PLS) in multivariate classification and calibration. We propose that by inspecting the orthogonal variation, both model interpretation and information quality are improved by enhancement of the resulting level of knowledge. Although the predictive capacity of PLS using orthogonal methods may be identical to that of PLS alone, the combined result can be superior when it comes to the model interpretation. By discussing theory and examples, several new advantages revealed by inspection of orthogonal variation are highlighted. Copyright (c) 2012 John Wiley & Sons, Ltd.
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| 5. |
- Pinto, Rui Climaco, 1972-, et al.
(author)
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Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis
- 2012
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In: Analytical Chemistry. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 84:20, s. 8675-8681
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Journal article (peer-reviewed)abstract
- We have developed a multistep strategy that integrates data from several large-scale experiments that suffer from systematic between-experiment variation. This strategy removes such variation that would otherwise mask differences of interest. It was applied to the evaluation of wood chemical analysis of 736 hybrid aspen trees: wild-type controls and transgenic trees potentially involved in wood formation. The trees were grown in four different greenhouse experiments imposing significant variation between experiments. Pyrolysis coupled to gas chromatography/mass spectrometry (Py-GC/MS) was used as a high throughput-screening platform for fingerprinting of wood chemotype. Our proposed strategy includes quality control, outlier detection, gene specific classification, and consensus analysis. The orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to generate the consensus chemotype profiles for each transgenic line. These were thereafter compiled to generate a global dataset. Multivariate analysis and cluster analysis techniques revealed a drastic reduction in between-experiment variation that enabled a global analysis of all transgenic lines from the four independent experiments. Information from in-depth analysis of specific transgenic lines and independent peak identification validated our proposed strategy.
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| 6. |
- Rui Climaco, Pinto, et al.
(author)
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Design of experiments on 135 cloned poplar trees to map environmental influence in greenhouse
- 2011
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In: Analytica Chimica Acta. - : Elsevier. - 0003-2670 .- 1873-4324. ; 685:2, s. 127-131
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Journal article (peer-reviewed)abstract
- To find and ascertain phenotypic differences, minimal variation between biological replicates is always desired. Variation between the replicates can originate from genetic transformation but also from environmental effects in the greenhouse. Design of experiments (DoE) has been used in field trials for many years and proven its value but is underused within functional genomics including greenhouse experiments. We propose a strategy to estimate the effect of environmental factors with the ultimate goal of minimizing variation between biological replicates, based on DoE. DoE can be analyzed in many ways. We present a graphical solution together with solutions based on classical statistics as well as the newly developed OPLS methodology.In this study, we used DoE to evaluate the influence of plant specific factors (plant size, shoot type, plant quality, amount of fertilizer) and rotation of plant positions on height and section area of 135 cloned wild type poplar trees grown in the greenhouse. Statistical analysis revealed that plant position was the main contributor to variability among biological replicates and applying a plant rotation scheme could reduce this variation.
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| 7. |
- Rådjursöga, Millie, 1977, et al.
(author)
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Metabolic profiles from two different breakfast meals characterized by H-1 NMR-based metabolomics
- 2017
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In: Food Chemistry. - : Elsevier BV. - 0308-8146 .- 1873-7072. ; 231, s. 267-274
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Journal article (peer-reviewed)abstract
- It is challenging to measure dietary exposure with techniques that are both accurate and applicable to free-living individuals. We performed a cross-over intervention, with 24 healthy individuals, to capture the acute metabolic response of a cereal breakfast (CB) and an egg and ham breakfast (EHB). Fasting and postprandial urine samples were analyzed using H-1 nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis. Metabolic profiles were distinguished in relation to ingestion of either CB or EHB. Phosphocreatine/creatine and citrate were identified at higher concentrations after consumption of EHB. Beverage consumption (i.e., tea or coffee) could clearly be seen in the data. 2-furoylglycine and 5-hydroxymethyl-2-furoic acid - potential biomarkers for coffee consumption were identified at higher concentrations in coffee drinkers. Thus H-1 NMR urine metabolomics is applicable in the characterization of acute metabolic fingerprints from meal consumption and in the identification of metabolites that may serve as potential biomarkers. (C) 2017 Elsevier Ltd. All rights reserved.
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