| 1. |
- Dhillon, Sundeep S., et al.
(författare)
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Metabolic profiling of zebrafish embryo development from blastula period to early larval stages
- 2019
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Ingår i: PLOS ONE. - San Francisco : Public Library of Science. - 1932-6203. ; 14:5
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Tidskriftsartikel (refereegranskat)abstract
- The zebrafish embryo is a popular model for drug screening, disease modelling and molecular genetics. In this study, samples were obtained from zebrafish at different developmental stages. The stages that were chosen were 3/4, 4/5, 24, 48, 72 and 96 hours post fertilization (hpf). Each sample included fifty embryos. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). Principle component analysis (PCA) was applied to get an overview of the data and orthogonal projection to latent structure discriminant analysis (OPLS-DA) was utilised to discriminate between the developmental stages. In this way, changes in metabolite profiles during vertebrate development could be identified. Using a GC-TOF-MS metabolomics approach it was found that nucleotides and metabolic fuel (glucose) were elevated at early stages of embryogenesis, whereas at later stages amino acids and intermediates in the Krebs cycle were abundant. This agrees with zebrafish developmental biology, as organs such as the liver and pancreas develop at later stages. Thus, metabolomics of zebrafish embryos offers a unique opportunity to investigate large scale changes in metabolic processes during important developmental stages in vertebrate development. In terms of stability of the metabolic profile and viability of the embryos, it was concluded at 72 hpf was a suitable time point for the use of zebrafish as a model system in numerous scientific applications.
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| 2. |
- Lundstedt-Enkel, Katrin, et al.
(författare)
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A Statistical Resampling Method To Calculate Biomagnification Factors Exemplified with Organochlorine Data from Herring (Clupea harengus) Muscle and Guillemot (Uria aalge) Egg from the Baltic Sea
- 2005
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Ingår i: ENVIRONMENTAL SCIENCE & TECHNOLOGY. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 39:21, s. 8395-8402
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Tidskriftsartikel (refereegranskat)abstract
- A novel method for calculating biomagnification factors is presented and demonstrated using contaminant concentration data from the Swedish national monitoring program regarding organochlorine contaminants (OCs) in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg, sampled from 1996 to 1999 from the Baltic Sea. With this randomly sampled ratios (RSR) method, biomagnification factors (BMFRSR) were generated and denoted with standard deviation (0) as a measure of the variation. The BMFRSR were calculated by randomly selecting one guillemot egg out of a total of 29 and one herring out of a total of 74, and the ratio was determined between the concentration of a given OC in that egg and the concentration of the same OC in that herring. With the resampling technique, this was performed 50 000 times for any given OC, and from this new distribution of ratios, BMFRSR for each OC were calculated and given as geometric mean (GM) with GM standard deviation (GMSD) range, arithmetic mean (AM) with AMSD range, and minimum (BMFMIN) as well as maximum (BMFMAX) biomagnification factors. The 14 analyzed OCs were p,p'DDT and its metabolites p,p'DDE and p,p'DDD, polychlorinated biphenyls (PCB congeners: CB28, CB52, CB101, CB118, CB138, CB153, and CB180), hexachlorocyclohexane isomers (alpha-, beta-, and gamma HCH), and hexachlorobenzene (HCB). Multivariate data analysis (MVDA) methods, including principal components analysis (PCA), partial least squares regression (PLS), and PLS discriminant analyses (PLS-DA), were first used to extract information from the complex biological and chemical data generated from each individual animal. MVDA were used to model similarities/dissimilarities regarding species (PCA, PLS-DA), sample years (PLS), and sample location (PLS-DA) to give a deeper understanding of the data that the BMF modeling was based upon. Contaminants that biomagnify, that had BMFRSR significantly higher than one, were p,p'DDE, CB118, HCB, CB138, CB180, CB153, beta HCH, and CB28. The contaminants that did not biomagnify were p,p'DDT, p,p'DDD, alpha HCH, CB101, and CB52. Eventual biomagnification for gamma HCH could not be determined. The BMFRSR for OCs present in herring muscle and guillemot egg showed a broad span with large variations for each contaminant. To be able to make reliable calculations of BMFs for different contaminants, we emphasize the importance of using data based upon large numbers of, as well as well-defined, individuals.
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| 6. |
- Torell, Frida, et al.
(författare)
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Metabolic Profiling of Multiorgan Samples : Evaluation of MODY5/RCAD Mutant Mice
- 2018
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 17:7, s. 2293-2306
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Tidskriftsartikel (refereegranskat)abstract
- In the present study, we performed a metabolomics analysis to evaluate a MODY5/RCAD mouse mutant line as a potential model for HNF1B-associated diseases. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) of gut, kidney, liver, muscle, pancreas, and plasma samples uncovered the tissue specific metabolite distribution. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was used to identify the differences between MODY5/RCAD and wild-type mice in each of the tissues. The differences included, for example, increased levels of amino acids in the kidneys and reduced levels of fatty acids in the muscles of the MODY5/RCAD mice. Interestingly, campesterol was found in higher concentrations in the MODY5/RCAD mice, with a four-fold and three-fold increase in kidneys and pancreas, respectively. As expected, the MODY5/RCAD mice displayed signs of impaired renal function in addition to disturbed liver lipid metabolism, with increased lipid and fatty acid accumulation in the liver. From a metabolomics perspective, the MODY5/RCAD model was proven to display a metabolic pattern similar to what would be suspected in HNF1B-associated diseases. These findings were in line with the presumed outcome of the mutation based on the different anatomy and function of the tissues as well as the effect of the mutation on development.
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| 7. |
- Torell, Frida, et al.
(författare)
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Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling
- 2015
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- Hierarchical modelling was applied in order to identify the organs that contribute to the levels of metabolites in plasma. Plasma and organ samples from gut, kidney, liver, muscle and pancreas were obtained from mice. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS) at the Swedish Metabolomics centre, Umea University, Sweden. The multivariate analysis was performed by means of principal component analysis (PCA) and orthogonal projections to latent structures (OPLS). The main goal of this study was to investigate how each organ contributes to the metabolic plasma profile. This was performed using hierarchical modelling. Each organ was found to have a unique metabolic profile. The hierarchical modelling showed that the gut, kidney and liver demonstrated the greatest contribution to the metabolic pattern of plasma. For example, we found that metabolites were absorbed in the gut and transported to the plasma. The kidneys excrete branched chain amino acids (BCAAs) and fatty acids are transported in the plasma to the muscles and liver. Lactic acid was also found to be transported from the pancreas to plasma. The results indicated that hierarchical modelling can be utilized to identify the organ contribution of unknown metabolites to the metabolic profile of plasma.
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| 8. |
- Torell, Frida, et al.
(författare)
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The effects of thawing on the plasma metabolome : evaluating differences between thawed plasma and multi-organ samples
- 2017
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Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 13:6
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Post-collection handling, storage and transportation can affect the quality of blood samples. Pre-analytical biases can easily be introduced and can jeopardize accurate profiling of the plasma metabolome. Consequently, a mouse study must be carefully planned in order to avoid any kind of bias that can be introduced, in order not to compromise the outcome of the study. The storage and shipment of the samples should be made in such a way that the freeze–thaw cycles are kept to a minimum. In order to keep the latent effects on the stability of the blood metabolome to a minimum it is essential to study the effect that the post-collection and pre-analytical error have on the metabolome. Objectives: The aim of this study was to investigate the effects of thawing on the metabolic profiles of different sample types. Methods: In the present study, a metabolomics approach was utilized to obtain a thawing profile of plasma samples obtained on three different days of experiment. The plasma samples were collected from the tail on day 1 and 3, while retro-orbital sampling was used on day 5. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS). Results: The thawed plasma samples were found to be characterized by higher levels of amino acids, fatty acids, glycerol metabolites and purine and pyrimidine metabolites as a result of protein degradation, cell degradation and increased phospholipase activity. The consensus profile was thereafter compared to the previously published study comparing thawing profiles of tissue samples from gut, kidney, liver, muscle and pancreas. Conclusions: The comparison between thawed organ samples and thawed plasma samples indicate that the organ samples are more sensitive to thawing, however thawing still affected all investigated sample types.
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| 9. |
- Torell, Frida, 1988-, et al.
(författare)
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Tissue sample stability : thawing effect on multi-organ samples
- 2016
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Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 12:2
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Tidskriftsartikel (refereegranskat)abstract
- Correct handling of samples is essential in metabolomic studies. Improper handling and prolonged storage of samples has unwanted effects on the metabolite levels. The aim of this study was to identify the effects that thawing has on different organ samples. Organ samples from gut, kidney, liver, muscle and pancreas were analyzed for a number of endogenous metabolites in an untargeted metabolomics approach, using gas chromatography time of flight mass spectrometry at the Swedish Metabolomics Centre, Umeå University, Sweden. Multivariate data analysis was performed by means of principal component analysis and orthogonal projection to latent structures discriminant analysis. The results showed that the metabolic changes caused by thawing were almost identical for all organs. As expected, there was a marked increase in overall metabolite levels after thawing, caused by increased protein and cell degradation. Cholesterol was one of the eight metabolites found to be decreased in the thawed samples in all organ groups. The results also indicated that the muscles are less susceptible to oxidation compared to the rest of the organ samples.
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