| 1. |
- Thuvander, A., et al.
(author)
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Levels of ochratoxin A in blood from Norwegian and Swedish blood donors and their possible correlation with food consumption
- 2001
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In: Food and Chemical Toxicology. - 0278-6915 .- 1873-6351. ; 39:12, s. 1145-1151
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Journal article (peer-reviewed)abstract
- Blood levels of ochratoxin A were determined in 406 Scandinavian blood donors (206 from Oslo, Norway, and 200 from Visby on the island of Gotland, Sweden), using an HPLC method. In connection with the blood collection, the subjects were asked to fill in a food questionnaire to obtain individual dietary information relevant to ochratoxin A exposure. The mean plasma level of ochratoxin A was 0.18 ng/ml in Oslo and slightly higher, 0.21 ng/ml (P = 0.046) in Visby. There was no correlation between plasma levels of ochratoxin A and the estimated total dietary intake of ochratoxin A based on consumption data and levels in food (retrieved from the literature), neither was the plasma level of ochratoxin A correlated with the total amount of food consumed. However, consumption of several foods, including cereal products, wine, beer and pork, were to some minor degree related to high plasma levels of ochratoxin A. The strongest correlations (correlation coefficient r >0.4; P <0.001) were observed for women in relation to the consumption of beer or medium brown bread. Correlation analysis of combinations of two or more food categories did not result in any statistically significant correlation.
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| 2. |
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| 3. |
- Biong, A. S., et al.
(author)
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Intake of dairy fat and dairy products, and risk of myocardial infarction: A case-control study
- 2008
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In: International Journal of Food Sciences and Nutrition. - : Informa UK Limited. - 0963-7486 .- 1465-3478. ; 59:2, s. 1-11
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Journal article (peer-reviewed)abstract
- The role of dairy fat in the aetiology of myocardial infarction (MI) is controversial. The aim of this study was to evaluate the association between intake of dairy fat and dairy products, and risk of a first acute MI. A total of 111 MI patients with a first acute MI and 107 population controls (men and women, age 45-75 years) were studied. Diet was assessed using a 180-item food frequency questionnaire. The MI cases had higher intake of total fat, but lower intake of saturated fat and dairy fat than the control persons. No effect of dairy fat or saturated fat on the odds ratio for MI was observed, however. A significant inverse trend in odds of MI for intake of cheese was observed, but the trend was no longer significant after adjustment for smoking. The results suggest that intake of fat from dairy products may not be associated with increased risk of having a first MI. The healthy control persons had a diet that differed from the diet of the MI patients in many aspects, and dairy products were a part of this diet. This may have protected them from having a first MI.
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| 4. |
- Blaise, Benjamin J., et al.
(author)
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Statistical analysis in metabolic phenotyping
- 2021
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In: Nature Protocols. - : Nature Publishing Group. - 1754-2189 .- 1750-2799. ; 16:9, s. 4299-4326
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Research review (peer-reviewed)abstract
- Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.
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| 5. |
- Bygdell, Joakim, et al.
(author)
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Protein expression in tension wood formation monitored at high tissue resolution in Populus
- 2017
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In: Journal of Experimental Botany. - : Oxford University Press. - 0022-0957 .- 1460-2431. ; 68:13, s. 3405-3417
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Journal article (peer-reviewed)abstract
- Tension wood (TW) is a specialized tissue with contractile properties that is formed by the vascular cambium in response to gravitational stimuli. We quantitatively analysed the proteomes of Populus tremula cambium and its xylem cell derivatives in stems forming normal wood (NW) and TW to reveal the mechanisms underlying TW formation. Phloem-, cambium-, and wood-forming tissues were sampled by tangential cryosectioning and pooled into nine independent samples. The proteomes of TW and NW samples were similar in the phloem and cambium samples, but diverged early during xylogenesis, demonstrating that reprogramming is an integral part of TW formation. For example, 14-3-3, reactive oxygen species, ribosomal and ATPase complex proteins were found to be up-regulated at early stages of xylem differentiation during TW formation. At later stages of xylem differentiation, proteins involved in the biosynthesis of cellulose and enzymes involved in the biosynthesis of rhamnogalacturonan-I, rhamnogalacturonan-II, arabinogalactan-II and fasciclin-like arabinogalactan proteins were up-regulated in TW. Surprisingly, two isoforms of exostosin family proteins with putative xylan xylosyl transferase function and several lignin biosynthesis proteins were also up-regulated, even though xylan and lignin are known to be less abundant in TW than in NW. These data provided new insight into the processes behind TW formation.
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| 6. |
- Bylesjö, Max, et al.
(author)
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K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space
- 2008
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In: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 9, s. 1-7
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Journal article (peer-reviewed)abstract
- Background: Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.Results: We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at http://www.sourceforge.net/projects/kopls/. The package includes essential functionality and documentation for model evaluation (using cross-validation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and plot functions to simplify the visualisation of data, e.g. for detecting trends or for identification of outlying samples. The utility of the software package is demonstrated by means of a metabolic profiling data set from a biological study of hybrid aspen.Conclusion: The properties of the K-OPLS method are well suited for analysis of biological data, which in conjunction with the availability of the outlined open-source package provides a comprehensive solution for kernel-based analysis in bioinformatics applications.
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| 7. |
- Bylesjö, Max, et al.
(author)
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OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification
- 2006
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In: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 341-351
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Journal article (peer-reviewed)abstract
- The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results.
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| 8. |
- Cloarec, Olivier, et al.
(author)
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Evaluation of the Orthogonal Projection on Latent Structure Model Limitations Caused by Chemical Shift Variability and Improved Visualization of Biomarker Changes in 1H NMR Spectroscopic Metabonomic Studies
- 2005
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In: Analytical Chemistry. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 77:2, s. 517-26
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Journal article (peer-reviewed)abstract
- In general, applications of metabonomics using biofluid NMR spectroscopic analysis for probing abnormal biochemical profiles in disease or due to toxicity have all relied on the use of chemometric techniques for sample classification. However, the well-known variability of some chemical shifts in 1H NMR spectra of biofluids due to environmental differences such as pH variation, when coupled with the large number of variables in such spectra, has led to the situation where it is necessary to reduce the size of the spectra or to attempt to align the shifting peaks, to get more robust and interpretable chemometric models. Here, a new approach that avoids this problem is demonstrated and shows that, moreover, inclusion of variable peak position data can be beneficial and can lead to useful biochemical information. The interpretation of chemometric models using combined back-scaled loading plots and variable weights demonstrates that this peak position variation can be handled successfully and also often provides additional information on the physicochemical variations in metabonomic data sets.
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| 9. |
- Goodacre, Royston, et al.
(author)
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Proposed minimum reporting standards for data analysis in metabolomics
- 2007
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In: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 3, s. 231-41
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Journal article (peer-reviewed)abstract
- The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual representations and hypotheses obtained from the data analyses.
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| 10. |
- Idborg, Helena, et al.
(author)
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STRATIFICATION OF SLE PATIENTS FOR IMPROVED DIAGNOSIS AND TREATMENT
- 2013
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In: Annals of the Rheumatic Diseases. - : BMJ. - 0003-4967 .- 1468-2060. ; 72, s. A80-A80
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Journal article (other academic/artistic)abstract
- Background. Systemic autoimmune diseases (SAIDs) affect about 2% of the population in Western countries. Sufficient diagnostic criteria are lacking due to the heterogeneity within diagnostic categories and apparent overlap regarding symptoms and patterns of autoantibodies between different diagnoses. Systemic lupus erythematosus (SLE) is regarded as a prototype for SAIDs and we hypothesise that subgroups of patients with SLE may have different pathogenesis and should consequently be subject to different treatment strategies.Objectives. Our goal is to find new biomarkers to be used for the identification of more homogenous patient populations for clinical trials and to identify sub-groups of patients with high risk of for example cardiovascular events.Methods. In this study we have utilised 320 SLE patients from the Karolinska lupus cohort and 320 age and gender matched controls. The SLE cohort was characterised based on clinical, genetic and serological data and combined by multivariate data analysis in a systems biology approach to study possible subgroups. A pilot study was designed to verify and investigate suggested subgroups of SLE. Two main subgroups were defined: One group was defined as having SSA and SSB antibodies and a negative lupus anticoagulant test (LAC), i.e., a “Sjögren-like” group. The other group was defined as being negative for SSA and SSB antibodies but positive in the LAC test.i.e. an “APS-like” group. EDTA-plasma from selected patients in these two groups and controls were analysed using a mass spectrometry (MS) based proteomic and metabolomic approach. Pathway analysis was then performed on the obtained data.Results. Our pilot study showed that differences in levels of proteins and metabolites could separate disease groups from population controls. The profile/pattern of involved factors in the complement system supported a division of SLE in two major subgroups, although each individual factor was not significantly different between subgroups. Complement factor 2 (C2) and membrane attack complex (MAC) were analysed in the entire cohort with complementary methods and C2 verifies our results while the levels of MAC did not differ between SLE subgroups. The generated metabolomics data clearly separated SLE patients from controls in both gas chromatography (GC)-MS and liquid chromatography (LC)-MS data. We found for example that tryptophan was lower in the SLE patients compared to controls.Conclusions. Our systems biology approach may lead to a better understanding of the disease and its pathogenesis, and assigning patients into subgroups will result in improved diagnosis and better outcome measures of SLE.
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| 11. |
- Jonsson, Pär, et al.
(author)
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Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets
- 2005
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In: The Analyst. - : Royal Society of Chemistry (RSC). - 0003-2654 .- 1364-5528. ; 130:5, s. 701-707
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Journal article (pop. science, debate, etc.)abstract
- LC/MS is an analytical technique that, due to its high sensitivity, has become increasingly popular for the generation of metabolic signatures in biological samples and for the building of metabolic data bases. However, to be able to create robust and interpretable ( transparent) multivariate models for the comparison of many samples, the data must fulfil certain specific criteria: (i) that each sample is characterized by the same number of variables, (ii) that each of these variables is represented across all observations, and (iii) that a variable in one sample has the same biological meaning or represents the same metabolite in all other samples. In addition, the obtained models must have the ability to make predictions of, e. g. related and independent samples characterized accordingly to the model samples. This method involves the construction of a representative data set, including automatic peak detection, alignment, setting of retention time windows, summing in the chromatographic dimension and data compression by means of alternating regression, where the relevant metabolic variation is retained for further modelling using multivariate analysis. This approach has the advantage of allowing the comparison of large numbers of samples based on their LC/MS metabolic profiles, but also of creating a means for the interpretation of the investigated biological system. This includes finding relevant systematic patterns among samples, identifying influential variables, verifying the findings in the raw data, and finally using the models for predictions. The presented strategy was here applied to a population study using urine samples from two cohorts, Shanxi (People's Republic of China) and Honolulu ( USA). The results showed that the evaluation of the extracted information data using partial least square discriminant analysis (PLS-DA) provided a robust, predictive and transparent model for the metabolic differences between the two populations. The presented findings suggest that this is a general approach for data handling, analysis, and evaluation of large metabolic LC/MS data sets.
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| 12. |
- Rantalainen, Mattias, et al.
(author)
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Kernel-based orthogonal projections to latent structures (K-OPLS)
- 2007
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In: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 21:7-9, s. 379-385
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Journal article (peer-reviewed)abstract
- The orthogonal projections to latent structures (OPLS) method has been successfully applied in various chemical and biological systems for modeling and interpretation of linear relationships between a descriptor matrix and response matrix. A kernel-based reformulation of the original OPLS algorithm is presented where the kernel Gram matrix is utilized as a replacement for the descriptor matrix. This enables usage of the kernel trick to efficiently transform the data into a higher-dimensional feature space where predictive and response-orthogonal components are calculated. This strategy has the capacity to improve predictive performance considerably in situations where strong non-linear relationships exist between descriptor and response variables while retaining the OPLS model framework. We put particular focus on describing properties of the rearranged algorithm in relation to the original OPLS algorithm. Four separate problems, two simulated and two real spectroscopic data sets, are employed to illustrate how the algorithm enables separate modeling of predictive and response-orthogonal variation in the feature space. This separation can be highly beneficial for model interpretation purposes while providing a flexible framework for supervised regression.
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| 13. |
- Rantalainen, Mattias, et al.
(author)
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Piecewise multivariate modelling of sequential metabolic profiling data
- 2008
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In: BMC Bioinformatics. - : EMBO. - 1471-2105. ; 9, s. 105-
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Journal article (peer-reviewed)abstract
- Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. Results: A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. Conclusion: The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.
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| 14. |
- Rantalainen, Mattias, et al.
(author)
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Statistically Integrated Metabonomic-Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice
- 2006
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 10, s. 2642-55
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Journal article (peer-reviewed)abstract
- A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies
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| 15. |
- Rosendal, Ebba, et al.
(author)
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Serine Protease Inhibitors Restrict Host Susceptibility to SARS-CoV-2 Infections
- 2022
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In: mBio. - : American Society for Microbiology. - 2161-2129 .- 2150-7511. ; 13:3
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Journal article (peer-reviewed)abstract
- The coronavirus disease 2019, COVID-19, is a complex disease with a wide range of symptoms from asymptomatic infections to severe acute respiratory syndrome with lethal outcome. Individual factors such as age, sex, and comorbidities increase the risk for severe infections, but other aspects, such as genetic variations, are also likely to affect the susceptibility to SARS-CoV-2 infection and disease severity. Here, we used a human 3D lung cell model based on primary cells derived from multiple donors to identity host factors that regulate SARS-CoV-2 infection. With a transcriptomics-based approach, we found that less susceptible donors show a higher expression level of serine protease inhibitors SERPINA1, SERPINE1, and SERPINE2, identifying variation in cellular serpin levels as restricting host factors for SARS-CoV-2 infection. We pinpoint their antiviral mechanism of action to inhibition of the cellular serine protease, TMPRSS2, thereby preventing cleavage of the viral spike protein and TMPRSS2-mediated entry into the target cells. By means of single-cell RNA sequencing, we further locate the expression of the individual serpins to basal, ciliated, club, and goblet cells. Our results add to the importance of genetic variations as determinants for SARS-CoV-2 susceptibility and suggest that genetic deficiencies of cellular serpins might represent risk factors for severe COVID-19. Our study further highlights TMPRSS2 as a promising target for antiviral intervention and opens the door for the usage of locally administered serpins as a treatment against COVID-19.
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| 16. |
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| 17. |
- TISCHER, M, et al.
(author)
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ENHANCEMENT OF ORBITAL MAGNETISM AT SURFACES - CO ON CU(100)
- 1995
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In: PHYSICAL REVIEW LETTERS. - : AMER INST PHYSICS. - 0031-9007. ; 75:8, s. 1602-1605
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Journal article (other academic/artistic)abstract
- By combining magnetic circular x-ray dichroism experiments with first principles electronic structure calculations, we demonstrate that the orbital contribution to magnetism can be strongly enhanced at surfaces. This effect is illustrated for Co grown on
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| 18. |
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