30 results found
Ostman JR, Pinto RC, Ebbels TMD, et al., 2022, Identification of prediagnostic metabolites associated with prostate cancer risk by untargeted mass spectrometry-based metabolomics: A case-control study nested in the Northern Sweden Health and Disease Study, INTERNATIONAL JOURNAL OF CANCER, Vol: 151, Pages: 2115-2127, ISSN: 0020-7136
Climaco Pinto R, Karaman I, Lewis MR, et al., 2022, Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets., Analytical Chemistry, Vol: 94, Pages: 5493-5503, ISSN: 0003-2700
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.
Pazoki R, Elliott J, Evangelou E, et al., 2021, Genetic analysis in European ancestry individuals identifies 517 loci associated with liver enzymes, Nature Communications, Vol: 12, ISSN: 2041-1723
Serum concentration of hepatic enzymes are linked to liver dysfunction, metabolic and cardiovascular diseases. We perform genetic analysis on serum levels of alanine transaminase (ALT), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) using data on 437,438 UK Biobank participants. Replication in 315,572 individuals from European descent from the Million Veteran Program, Rotterdam Study and Lifeline study confirms 517 liver enzyme SNPs. Genetic risk score analysis using the identified SNPs is strongly associated with serum activity of liver enzymes in two independent European descent studies (The Airwave Health Monitoring study and the Northern Finland Birth Cohort 1966). Gene-set enrichment analysis using the identified SNPs highlights involvement in liver development and function, lipid metabolism, insulin resistance, and vascular formation. Mendelian randomization analysis shows association of liver enzyme variants with coronary heart disease and ischemic stroke. Genetic risk score for elevated serum activity of liver enzymes is associated with higher fat percentage of body, trunk, and liver and body mass index. Our study highlights the role of molecular pathways regulated by the liver in metabolic disorders and cardiovascular disease.
Wu C-T, Wang Y, Wang Y, et al., 2020, Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection, BIOINFORMATICS, Vol: 36, Pages: 2862-2871, ISSN: 1367-4803
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Pazoki R, Evangelou E, Mosen-Ansorena D, et al., 2019, GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits, Nature Communications, Vol: 10, ISSN: 2041-1723
Urinary sodium and potassium excretion are associated with blood pressure (BP) and cardiovascular disease (CVD). The exact biological link between these traits is yet to be elucidated. Here, we identify 51 loci for sodium and 13 for potassium excretion in a large-scale genome-wide association study (GWAS) on urinary sodium and potassium excretion using data from 446,237 individuals of European descent from the UK Biobank study. We extensively interrogate the results using multiple analyses such as Mendelian randomization, functional assessment, co localization, genetic risk score, and pathway analyses. We identify a shared genetic component between urinary sodium and potassium expression and cardiovascular traits. Ingenuity pathway analysis shows that urinary sodium and potassium excretion loci are over represented in behavioural response to stimuli. Our study highlights pathways that are shared between urinary sodium and potassium excretion and cardiovascular traits.
Djekic D, Pinto R, Repsilber D, et al., 2019, Serum untargeted lipidomic profiling reveals dysfunction of phospholipid metabolism in subclinical coronary artery disease, Vascular Health and Risk Management, Vol: 15, Pages: 123-135, ISSN: 1176-6344
Purpose: Disturbed metabolism of cholesterol and triacylglycerols (TGs) carries increased risk for coronary artery calcification (CAC). However, the exact relationship between individual lipid species and CAC remains unclear. The aim of this study was to identify disturbances in lipid profiles involved in the calcification process, in an attempt to propose potential biomarker candidates. Patients and methods: We studied 70 patients at intermediate risk for coronary artery disease who had undergone coronary calcification assessment using computed tomography and Agatston coronary artery calcium score (CACS). Patients were divided into three groups: with no coronary calcification (NCC; CACS: 0; n=26), mild coronary calcification (MCC; CACS: 1-250; n=27), or severe coronary calcification (SCC; CACS: >250; n=17). Patients' serum samples were analyzed using liquid chromatography-mass spectrometry in an untargeted lipidomics approach. Results: We identified 103 lipids within the glycerolipid, glycerophospholipid, sphingolipid, and sterol lipid classes. After false discovery rate correction, phosphatidylcholine (PC)(16:0/20:4) in higher levels and PC(18:2/18:2), PC(36:3), and phosphatidylethanolamine(20:0/18:2) in lower levels were identified as correlates with SCC compared to NCC. There were no significant differences in the levels of individual TGs between the three groups; however, clustering the lipid profiles showed a trend for higher levels of saturated and monounsaturated TGs in SCC compared to NCC. There was also a trend for lower TG(49:2), TG(51:1), TG(54:5), and TG(56:8) levels in SCC compared to MCC. Conclusion: In this study we investigated the lipidome of patients with coronary calcification. Our results suggest that the calcification process may be associated with dysfunction in autophagy. The lipidomic biomarkers revealed in this study may aid in better assessment of patients with subclinical coronary artery disease.
Pinto RC, Karaman I, Fussell JC, et al., 2019, Applications of Metabolic Phenotyping in Epidemiology, HANDBOOK OF METABOLIC PHENOTYPING, Editors: Lindon, Nicholson, Holmes, Publisher: ELSEVIER SCIENCE BV, Pages: 491-534, ISBN: 978-0-12-812293-8
Pênčík A, Casanova-Sáez R, Pilařová V, et al., 2018, Ultra-rapid auxin metabolite profiling for high-throughput mutant screening in Arabidopsis, Journal of Experimental Botany, Vol: 69, Pages: 2569-2579, ISSN: 0022-0957
Published by Oxford University Press on behalf of the Society for Experimental Biology. Auxin (indole-3-acetic acid, IAA) plays fundamental roles as a signalling molecule during numerous plant growth and development processes. The formation of local auxin gradients and auxin maxima/minima, which is very important for these processes, is regulated by auxin metabolism (biosynthesis, degradation, and conjugation) as well as transport. When studying auxin metabolism pathways it is crucial to combine data obtained from genetic investigations with the identification and quantification of individual metabolites. Thus, to facilitate efforts to elucidate auxin metabolism and its roles in plants, we have developed a high-throughput method for simultaneously quantifying IAA and its key metabolites in minute samples (<10 mg FW) of Arabidopsis thaliana tissues by in-tip micro solid-phase extraction and fast LC-tandem MS. As a proof of concept, we applied the method to a collection of Arabidopsis mutant lines and identified lines with altered IAA metabolite profiles using multivariate data analysis. Finally, we explored the correlation between IAA metabolite profiles and IAA-related phenotypes. The developed rapid analysis of large numbers of samples (>100 samples d -1) is a valuable tool to screen for novel regulators of auxin metabolism and homeostasis among large collections of genotypes.
Lomnytska M, Pinto R, Becker S, et al., 2018, Platelet protein biomarker panel for ovarian cancer diagnosis, Biomarker Research, Vol: 6, ISSN: 2050-7771
Background: Platelets support cancer growth and spread making platelet proteins candidates in the search for biomarkers. Methods: Two-dimensional (2D) gel electrophoresis, Partial Least Squares Discriminant Analysis (PLS-DA), Western blot, DigiWest. Results: PLS-DA of platelet protein expression in 2D gels suggested differences between the International Federation of Gynaecology and Obstetrics (FIGO) stages III-IV of ovarian cancer, compared to benign adnexal lesions with a sensitivity of 96% and a specificity of 88%. A PLS-DA-based model correctly predicted 7 out of 8 cases of FIGO stages I-II of ovarian cancer after verification by western blot. Receiver-operator curve (ROC) analysis indicated a sensitivity of 83% and specificity of 76% at cut-off >0.5 (area under the curve (AUC) = 0.831, p < 0.0001) for detecting these cases. Validation on an independent set of samples by DigiWest with PLS-DA differentiated benign adnexal lesions and ovarian cancer, FIGO stages III-IV, with a sensitivity of 70% and a specificity of 83%. Conclusion: We identified a group of platelet protein biomarker candidates that can quantify the differential expression between ovarian cancer cases as compared to benign adnexal lesions.
Rådjursöga M, Karlsson GB, Lindqvist HM, et al., 2017, Metabolic profiles from two different breakfast meals characterized by 1H NMR-based metabolomics., Food Chem, Vol: 231, Pages: 267-274
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 1H 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 1H 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.
Magdalinou NK, Noyce AJ, Pinto R, et al., 2017, Identification of candidate cerebrospinal fluid biomarkers in parkinsonism using quantitative proteomics., Parkinsonism Relat Disord, Vol: 37, Pages: 65-71
INTRODUCTION: Neurodegenerative parkinsonian syndromes have significant clinical and pathological overlap, making early diagnosis difficult. Cerebrospinal fluid (CSF) biomarkers may aid the differentiation of these disorders, but other than α-synuclein and neurofilament light chain protein, which have limited diagnostic power, specific protein biomarkers remain elusive. OBJECTIVES: To study disease mechanisms and identify possible CSF diagnostic biomarkers through discovery proteomics, which discriminate parkinsonian syndromes from healthy controls. METHODS: CSF was collected consecutively from 134 participants; Parkinson's disease (n = 26), atypical parkinsonian syndromes (n = 78, including progressive supranuclear palsy (n = 36), multiple system atrophy (n = 28), corticobasal syndrome (n = 14)), and elderly healthy controls (n = 30). Participants were divided into a discovery and a validation set for analysis. The samples were subjected to tryptic digestion, followed by liquid chromatography-mass spectrometry analysis for identification and relative quantification by isobaric labelling. Candidate protein biomarkers were identified based on the relative abundances of the identified tryptic peptides. Their predictive performance was evaluated by analysis of the validation set. RESULTS: 79 tryptic peptides, derived from 26 proteins were found to differ significantly between atypical parkinsonism patients and controls. They included acute phase/inflammatory markers and neuronal/synaptic markers, which were respectively increased or decreased in atypical parkinsonism, while their levels in PD subjects were intermediate between controls and atypical parkinsonism. CONCLUSION: Using an unbiased proteomic approach, proteins were identified that were able to differentiate atypical parkinsonian syndrome patients from healthy controls. Our study indicates that markers that may reflect neuronal function and/or p
Boles U, Pinto RC, David S, et al., 2017, Dysregulated fatty acid metabolism in coronary ectasia: An extended lipidomic analysis., Int J Cardiol, Vol: 228, Pages: 303-308
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.
Pinto RC, 2017, Chemometrics Methods and Strategies in Metabolomics., Pages: 163-190
Chemometrics has been a fundamental discipline for the development of metabolomics, while symbiotically growing with it. From design of experiments, through data processing, to data analysis, chemometrics tools are used to design, process, visualize, explore and analyse metabolomics data.In this chapter, the most commonly used chemometrics methods for data analysis and interpretation of metabolomics experiments will be presented, with focus on multivariate analysis. These are projection-based linear methods, like principal component analysis (PCA) and orthogonal projection to latent structures (OPLS), which facilitate interpretation of the causes behind the observed sample trends, correlation with outcomes or group discrimination analysis. Validation procedures for multivariate methods will be presented and discussed.Univariate analysis is briefly discussed in the context of correlation-based linear regression methods to find associations to outcomes or in analysis of variance-based and logistic regression methods for class discrimination. These methods rely on frequentist statistics, with the determination of p-values and corresponding multiple correction procedures.Several strategies of design-analysis of metabolomics experiments will be discussed, in order to guide the reader through different setups, adopted to better address some experimental issues and to better test the scientific hypotheses.
Djekic D, Pinto R, Vorkas PA, et al., 2016, Replication of LC-MS untargeted lipidomics results in patients with calcific coronary disease: an interlaboratory reproducibility study, International Journal of Cardiology, Vol: 222, Pages: 1042-1048, ISSN: 1874-1754
BackgroundRecently a lipidomics approach was able to identify perturbed fatty acyl chain (FAC) and sphingolipid moieties that could stratify patients according to the severity of coronary calcification, a form of subclinical atherosclerosis. Nevertheless, these findings have not yet been reproduced before generalising their application. The aim of this study was to evaluate the reproducibility of lipidomics approaches by replicating previous lipidomic findings in groups of patients with calcific coronary artery disease (CCAD).MethodsPatients were separated into the following groups based on their calcium score (CS); no calcification (CS: 0; n = 26), mild calcification (CS: 1–250; n = 27) and severe calcification (CS: > 250; n = 17). Two serum samples were collected from each patient and used for comparative analyses by 2 different laboratories, in different countries and time points using liquid chromatography coupled to mass spectrometry untargeted lipidomics methods.ResultsSix identical metabolites differentiated patients with severe coronary artery calcification from those with no calcification were found by both laboratories independently. Additionally, relative intensities from the two analyses demonstrated high correlation coefficients. Phosphatidylcholine moieties with 18-carbon FAC were identified in lower intensities and 20:4 FAC in higher intensities in the serum of diseased group. Moreover, 3 common sphingomyelins were detected.ConclusionThis is the first interlaboratory reproducibility study utilising lipidomics applications in general and specifically in patients with CCAD. Lipid profiling applications in patients with CCAD are very reproducible in highly specialised and experienced laboratories and could be applied in clinical practice in order to spare patients diagnostic radiation.
Stenson M, Pedersen A, Hasselblom S, et al., 2016, Serum nuclear magnetic resonance-based metabolomics and outcome in diffuse large B-cell lymphoma patients - a pilot study., Leuk Lymphoma, Vol: 57, Pages: 1814-1822
The prognosis for diffuse large B-cell lymphoma (DLBCL) patients with early relapse or refractory disease is dismal. To determine if clinical outcome correlated to diverse serum metabolomic profiles, we used (1)H nuclear magnetic resonance (NMR) spectroscopy and compared two groups of DLBCL patients treated with immunochemotherapy: i) refractory/early relapse (REF/REL; n=27) and ii) long-term progression-free (CURED; n = 60). A supervised multivariate analysis showed a separation between the groups. Among discriminating metabolites higher in the REF/REL group were the amino acids lysine and arginine, the degradation product cadaverine and a compound in oxidative stress (2-hydroxybutyrate). In contrast, the amino acids aspartate, valine and ornithine, and a metabolite in the glutathione cycle, pyroglutamate, were higher in CURED patients. Together, our data indicate that NMR-based serum metabolomics can identify a signature for DLBCL patients with high-risk of failing immunochemotherapy, prompting for larger validating studies which could lead to more individualized treatment of this disease.
Karimpour M, Surowiec I, Wu J, et al., 2016, Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal., Anal Chim Acta, Vol: 908, Pages: 121-131
The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms - gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) - to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1-86% for GC-MS and 0.9-42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2-97% for the positive and 4-69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a multi-platform
Ross AB, Svelander C, Undeland I, et al., 2015, Herring and Beef Meals Lead to Differences in Plasma 2-Aminoadipic Acid, β-Alanine, 4-Hydroxyproline, Cetoleic Acid, and Docosahexaenoic Acid Concentrations in Overweight Men., J Nutr, Vol: 145, Pages: 2456-2463
BACKGROUND: Dietary guidelines generally recommend increasing fish intake and reducing red meat intake for better long-term health. Few studies have compared the metabolic differences between eating meat and fish. OBJECTIVE: The objective of this study was to determine whether there are differences in the postprandial plasma metabolic response to meals containing baked beef, baked herring, and pickled herring. METHODS: Seventeen overweight men (BMI 25-30 kg/m(2), 41-67 y of age) were included in a randomized crossover intervention study. Subjects ate baked herring-, pickled herring-, and baked beef-based meals in a randomized order and postprandial blood plasma samples were taken over 7 h. Plasma metabolomics were measured with the use of gas chromatography-mass spectrometry and areas under the curve for detected metabolites were compared between meals. RESULTS: The plasma postprandial response of 2-aminoadipic acid, a suggested marker of diabetes risk, was 1.6 times higher after the beef meal than after the baked herring meal (P < 0.001). Plasma β-alanine and 4-hydroxyproline both were markedly greater after beef intake than after herring intake (16 and 3.4 times the response of baked herring, respectively; P < 0.001). Herring intake led to a greater plasma postprandial response from docosahexaenoic acid (DHA) and cetoleic acid compared with beef (17.6 and 150 times greater, respectively; P < 0.001), whereas hippuric acid and benzoic acid were elevated after pickled herring compared with baked herring (5.4 and 43 times higher; P < 0.001). CONCLUSIONS: These results in overweight men confirm that DHA and cetoleic acid reflect herring intake, whereas β-alanine and 4-hydroxyproline are potential biomarkers for beef intake. The greater postprandial rise in 2-aminoadipic acid after the beef meal, coupled to its proposed role in stimulating insulin secretion, may have importance in the context of red meat intake and increased diabetes risk. This tri
Mattsson A, Kärrman A, Pinto R, et al., 2015, Metabolic Profiling of Chicken Embryos Exposed to Perfluorooctanoic Acid (PFOA) and Agonists to Peroxisome Proliferator-Activated Receptors., PLoS One, Vol: 10
Untargeted metabolic profiling of body fluids in experimental animals and humans exposed to chemicals may reveal early signs of toxicity and indicate toxicity pathways. Avian embryos develop separately from their mothers, which gives unique possibilities to study effects of chemicals during embryo development with minimal confounding factors from the mother. In this study we explored blood plasma and allantoic fluid from chicken embryos as matrices for revealing metabolic changes caused by exposure to chemicals during embryonic development. Embryos were exposed via egg injection on day 7 to the environmental pollutant perfluorooctanoic acid (PFOA), and effects on the metabolic profile on day 12 were compared with those caused by GW7647 and rosiglitazone, which are selective agonists to peroxisome-proliferator activated receptor α (PPARα) and PPARγ, respectively. Analysis of the metabolite concentrations from allantoic fluid by Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) showed clear separation between the embryos exposed to GW7647, rosiglitazone, and vehicle control, respectively. In blood plasma only GW7647 caused a significant effect on the metabolic profile. PFOA induced embryo mortality and increased relative liver weight at the highest dose. Sublethal doses of PFOA did not significantly affect the metabolic profile in either matrix, although single metabolites appeared to be altered. Neonatal mortality by PFOA in the mouse has been suggested to be mediated via activation of PPARα. However, we found no similarity in the metabolite profile of chicken embryos exposed to PFOA with those of embryos exposed to PPAR agonists. This indicates that PFOA does not activate PPAR pathways in our model at concentrations in eggs and embryos well above those found in wild birds. The present study suggests that allantoic fluid and plasma from chicken embryos are useful and complementary matrices for exploring effects on the metabolic
Larsson N, Lundström SL, Pinto R, et al., 2014, Lipid mediator profiles differ between lung compartments in asthmatic and healthy humans., Eur Respir J, Vol: 43, Pages: 453-463
Oxylipins are oxidised fatty acids that can exert lipid mediator functions in inflammation, and several oxylipins derived from arachidonic acid are linked to asthma. This study quantified oxylipin profiles in different regions of the lung to obtain a broad-scale characterisation of the allergic asthmatic inflammation in relation to healthy individuals. Bronchoalveolar lavage fluid (BALF), bronchial wash fluid and endobronchial mucosal biopsies were collected from 16 healthy and 16 mildly allergic asthmatic individuals. Inflammatory cell counts, immunohistochemical staining and oxylipin profiling were performed. Univariate and multivariate statistics were employed to evaluate compartment-dependent and diagnosis-dependent oxylipin profiles in relation to other measured parameters. Multivariate modelling showed significantly different bronchial wash fluid and BALF oxylipin profiles in both groups (R(2)Y[cum]=0.822 and Q(2)[cum]=0.759). Total oxylipin concentrations and five individual oxylipins, primarily from the lipoxygenase (LOX) pathway of arachidonic and linoleic acid, were elevated in bronchial wash fluid from asthmatics compared to that from healthy controls, supported by immunohistochemical staining of 15-LOX-1 in the bronchial epithelium. No difference between the groups was found among BALF oxylipins. In conclusion, bronchial wash fluid and BALF contain distinct oxylipin profiles, which may have ramifications for the study of respiratory diseases. Specific protocols for sampling proximal and distal airways separately should be employed for lipid mediator studies.
Pinto RC, Gerber L, Eliasson M, et al., 2012, Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis., Anal Chem, Vol: 84, Pages: 8675-8681
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.
Pinto RC, Trygg J, Gottfries J, 2012, Advantages of orthogonal inspection in chemometrics, Journal of Chemometrics, Vol: 26, Pages: 231-235, ISSN: 0886-9383
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 15years, 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. © 2012 John Wiley & Sons, Ltd.
Pinto RC, Stenlund H, Hertzberg M, et al., 2011, Design of experiments on 135 cloned poplar trees to map environmental influence in greenhouse., Anal Chim Acta, Vol: 685, Pages: 127-131
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, and 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.
Jouan-Rimbaud Bouveresse D, Pinto RC, Schmidtke LM, et al., 2011, Identification of significant factors by an extension of ANOVA-PCA based on multi-block analysis, Chemometrics and Intelligent Laboratory Systems, Vol: 106, Pages: 173-182, ISSN: 0169-7439
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. © 2010 Elsevier B.V.
Pinto RC, Locquet N, Eveleigh L, et al., 2010, Preliminary studies on the mid-infrared analysis of edible oils by direct heating on an ATR diamond crystal, Food Chemistry, Vol: 120, Pages: 1170-1177, ISSN: 0308-8146
In this work a new, easy and rapid MIR-ATR technique to monitor the thermal stability of oils is presented. The method uses a heated ATR apparatus set at selected temperatures to thermally modify the oils and simultaneously acquire spectra. Because of the larger sample surface to volume ratio, degradation reactions are faster compared to other heating methods. Three different edible oils (sunflower, olive and canola), are subjected to the method. Sunflower oil with or without tocopherol and at three different temperatures (130, 150 and 170 °C) was also analysed. Wavenumbers known to be relevant to oil degradation processes are selected to show the modifications in the spectra over time. © 2009 Elsevier Ltd. All rights reserved.
Climaco-Pinto R, Barros AS, Locquet N, et al., 2009, Improving the detection of significant factors using ANOVA-PCA by selective reduction of residual variability., Anal Chim Acta, Vol: 653, Pages: 131-142
Selective elimination of residual error can be used when applying Harrington's ANOVA-PCA in order to improve the capabilities of the method. ANOVA-PCA is sometimes unable to discriminate between levels of a factor when sources of high residual variability are present. In some cases this variability is not random, possesses some structure and is large enough to be responsible for the first principal components calculated by the PCA step in the ANOVA-PCA. This fact sometimes makes it impossible for the interesting variance to be in the first two PCA components. By using the proposed selective residuals elimination procedure, one may improve the ability of the method to detect significant factors as well as have an understanding of the different kinds of residual variance present in the data. Two datasets are used to show how the method is used in order to iteratively detect variance associated with the factors even when it is not initially visible. A permutation method is used to confirm that the observed significance of the factors was not accidental.
Climaco Pinto R, Bosc V, Noçairi H, et al., 2008, Using ANOVA-PCA for discriminant analysis: application to the study of mid-infrared spectra of carrageenan gels as a function of concentration and temperature., Anal Chim Acta, Vol: 629, Pages: 47-55
In this work the ANOVA-PCA method is applied to a MIR spectroscopy dataset of carrageenan in order to evaluate which of the factors within its fixed effects experimental design are significant in relation to the residual error. The factors defined in the experimental design are concentration (1% and 2%), temperature (30, 40, 45, 50, and 60 degrees C), day (1 and 2) and sample (20 samples, 3 repetitions). The two factors, concentration and temperature, were considered as significant and the main features related with its physico-chemical properties were identified. It is also of interest to acquire a better understanding of the interaction between concentration and temperature and its effect on the adhesion of gels onto the surface of contact. In fact, no significant interaction was found between the two factors, but it was shown that the factor temperature behaves in a non-linear way. As classification using the ANOVA-PCA procedure has not been developed until now, a new method is proposed for the classification of new samples in respect to the levels of each significant factor.
Barros AS, Pinto R, Bouveresse DJR, et al., 2008, Principal component transform - Outer product analysis in the PCA context, Chemometrics and Intelligent Laboratory Systems, Vol: 93, Pages: 43-48, ISSN: 0169-7439
Outer product analysis is a method that permits the combination of two spectral domains with the aim of emphasizing co-evolutions of spectral regions. This data fusion technique consists in the product of all combinations of the variables that define each spectral domain. The main issue concerning the application of this technique is the very wide data matrix obtained which can be very hard to handle with multivariate techniques such as PCA or PLS, due to computer resources constraints. The present work presents an alternative way to perform outer product analysis in the PCA context without incurring into high demands on computational resources. This works shows that by decomposing each spectral domain with PCA and performing the outer product on the recovered scores, one can obtain the same results as if one calculated the outer product in the original variable space, but using much less computational resources. The results show that this approach will make possible to apply outer product analysis to very wide domains. © 2008 Elsevier B.V. All rights reserved.
Barros AS, Pinto R, Delgadillo I, et al., 2007, Segmented Principal Component Transform-Partial Least Squares regression, Chemometrics and Intelligent Laboratory Systems, Vol: 89, Pages: 59-68, ISSN: 0169-7439
An approach for doing PLS on very wide datasets is proposed in this work. The method is based on the decomposition, by means of a SVD, of non-superimposed segments of the original data matrix. It is shown that this approach uses less computer resources compared to SIMPLS and PCT-PLS1. Furthermore, it is also shown that the results obtained by this approach are the same as those obtained by other regression methods (PLS and SIMPLS). The method implementation is simple and can be done in a distributed environment. © 2007 Elsevier B.V. All rights reserved.
Sarembaud J, Pinto R, Rutledge DN, et al., 2007, Application of the ANOVA-PCA method to stability studies of reference materials, Analytica Chimica Acta, Vol: 603, Pages: 147-154, ISSN: 0003-2670
Near infrared spectroscopy (NIRS) is an analytical technique that can be very useful for stability studies in particular because of its non destructive analytical capability. However, the spectral interpretation and treatment of this kind of multivariate data remains difficult without the use of chemometrics. In this article, a recent chemometrics method, analysis of variance - principal component analysis (ANOVA-PCA), was used for NIRS stability studies of sunflower and bread wheat external reference materials (ERM). It provided a practical tool for the study of the significance of various storage conditions according to an experimental design. Thus, the effect of the temperature, the nature of the atmosphere in the packaging and the storage duration were tested. ANOVA-PCA highlighted the influence of temperature and storage duration on the stability of the sunflower materials. For the bread wheat materials, the storage conditions did not have a significant effect on stability. Consequently, by applying ANOVA-PCA to near infrared spectral data, the sunflower materials were found to be considered stable for the time length of the study, i.e. 18 months stored in a cold room, while the bread wheat materials were found to be considered stable for the time length of the study, i.e. 12 months under the same conditions. © 2007 Elsevier B.V. All rights reserved.
Jaillais B, Pinto R, Barros AS, et al., 2005, Outer-product analysis (OPA) using PCA to study the influence of temperature on NIR spectra of water, Vibrational Spectroscopy, Vol: 39, Pages: 50-58, ISSN: 0924-2031
Outer-product analysis (OPA) is a method which makes it possible to emphasise co-evolutions of spectral regions in signals acquired in two different domains or even for the same domain. The calculation of the outer-product (OP) matrix linking the two domains corresponds to a mutual weighting of the two signals. Various statistical techniques - both univariate and multivariate - can be applied to these matrices to bring out these simultaneous variations. Applying principal components analysis (PCA) to the OP matrix allows to visualise both the distribution of the individuals (scores plots) and the simultaneous variations in the signals related to this distribution of the individuals (loadings). In the case of complex data sets with many sources of variation in the samples, partial least squares regression (PLS) can be used to direct the analysis in order to visualise those simultaneous variations that are associated with a particular evolution of the samples. In this study, we applied OPA to a data set concerning the evolution of near infrared (NIR) spectra of water as a function of temperature. The different loadings profiles obtained by PCA are compared with the synchronous and asynchronous variable-variable spectra obtained using 2D-correlation spectroscopy (2DCOS), in order to study the resemblances and the differences between the two techniques. The outer-product may also be calculated for the transposed matrices. In this case, the PCA loadings may be compared with the synchronous and asynchronous sample-sample spectra obtained by 2DCOS. © 2004 Elsevier B.V. All rights reserved.
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