Biomedical subjects

Oliver Fiehn

Publications and source records attributed to Oliver Fiehn.

At least 19 recordsLinked to original sources

PubMed2026

Lipidomic Profiling Reveals Differential Behaviors of Individual Free Fatty Acids During Altered Metabolic States in Rats.

We used lipidomic analyses to investigate how individual free fatty acids (FFAs) behave differently in metabolic states altered by diet and by antibiotic treatment (ABX) that depletes gut bacteria. Wistar rats were fed either a low-fat or high-fat purified diet, or standard chow with or without antibiotics for two weeks (n = 8-10). Blood samples were then collected before and after meals. Individual FFAs were quantified and grouped based on distinct postprandial response patterns across dietary and treatment conditions. Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), key ω-3 FFAs, exhibited postprandial shifts suggestive of suppressed adipocyte lipolysis following meals. Fatty acids in the high-fat diet (HFD) elevated postprandial FFA levels, masking the meal-induced suppression of lipolysis observed with chow or low-fat diet (LFD). Some FFAs, including medium-chain saturated species, remained unaffected by meals. We further evaluated the impact of diet and ABX on baseline (pre-meal) concentrations of FFAs. Certain FFAs were altered by purified diets compared to standard chow. Notably, EPA and DHA were selectively depleted under HFD conditions, likely due to enhanced catabolic activity. In conclusion, lipidomic profiling revealed divergent behaviors among individual FFAs, reflecting distinct metabolic processes and regulatory mechanisms under altered metabolic states.

Animals↗

PubMed2007

High copper selectively alters lipid metabolism and cell cycle machinery in the mouse model of Wilson disease.

Copper is essential for human physiology, but in excess it causes the severe metabolic disorder Wilson disease. Elevated copper is thought to induce pathological changes in tissues by stimulating the production of reactive oxygen species that damage multiple cell targets. To better understand the molecular basis of this disease, we performed genome-wide mRNA profiling as well as protein and metabolite analysis for Atp7b-/- mice, an animal model of Wilson disease. We found that at the presymptomatic stages of the disease, copper-induced changes are inconsistent with widespread radical-mediated damage, which is likely due to the sequestration of cytosolic copper by metallothioneins that are markedly up-regulated in Atp7b-/- livers. Instead, copper selectively up-regulates molecular machinery associated with the cell cycle and chromatin structure and down-regulates lipid metabolism, particularly cholesterol biosynthesis. Specific changes in the transcriptome are accompanied by distinct metabolic changes. Biochemical and mass spectroscopy measurements revealed a 3.6-fold decrease of very low density lipoprotein cholesterol in serum and a 33% decrease of liver cholesterol, indicative of a marked decrease in cholesterol biosynthesis. Consistent with low cholesterol levels, the amount of activated sterol regulatory-binding protein 2 (SREBP-2) is increased in Atp7b-/- nuclei. However, the SREBP-2 target genes are dysregulated suggesting that elevated copper alters SREBP-2 function rather than its processing or re-localization. Thus, in Atp7b-/- mice elevated copper affects specific cellular targets at the transcription and/or translation levels and has distinct effects on liver metabolic function, prior to appearance of histopathological changes. The identification of the network of specific copper-responsive targets facilitates further mechanistic analysis of human disorders of copper misbalance.

Adenosine Triphosphatases↗

PubMed2007

Metabolite profiling in blood plasma.

Metabolite profiling has been established as a multiparallel strategy for relative quantification of a mixture of compounds or compound classes using chromatography and universal detection technologies (gas chromatography-mass spectrometry [GC-MS], liquid chromatography-MS). Despite its origins dating back to the late 1960s, it was only in the 1980s that its use was acknowledged to diagnose metabolic disorders in men, especially for rapid screening of inborn errors. Even faster electrospray ionization-MS/MS screening methods replaced longish chromatographic methods, and method development had stopped despite its potential use for other, less imminent diseases such as likelihood assessments of type II diabetes mellitus or cardiovascular risk factor evaluation. In addition to its diagnostic use, profiling blood samples can be employed to investigate specific biochemical responses. The broader scope of analysis outweighs the disadvantages by taking compromises in method development and the reduced accuracy for specific metabolites. This chapter exemplifies the strategies in metabolite profiling by GC-MS. It gives experimental details on basic steps like blood plasma withdrawal, storage, protein precipitation, extraction, concentration, derivatization, data acquisition, raw data processing, and result data tranformation. A major difference to profiling plant tissues is that no fractionation step is utilized, enabling the analysis of primary metabolites like sugars and amino acids concomitant with lipids such as sterols and free fatty acids.

Amino Acids↗

PubMed2007

Application of liquid chromatography-mass spectrometry analysis in metabolomics: reversed-phase monolithic capillary chromatography and hydrophilic chromatography coupled to electrospray ionization-mass spectrometry.

Analysis of the entire metabolome as the sum of all detectable components in the sample rather than analysis of each individual metabolite is performed by the metabolomics approaches. To monitor in parallel hundreds or even thousands of metabolites, high-throughput techniques are required that enable screening for relative changes rather than absolute concentrations of compounds. Most analytical techniques for profiling small molecules consist of gas chromatography (GC) or high-performance liquid chromatography (HPLC) coupled to mass spectrometry. HPLC separations are better suited for the analysis of labile and high molecular weight compounds, and for the analysis of nonvolatile polar compounds in their natural form. Although GC- and HPLC-based profiling techniques are not truly quantitative, the compounds detecting and employing the acceptable standards may compare their relative amounts. We have demonstrated that reversed-phase monolithic capillary chromatography and hydrophilic chromatography can be successfully applied for sufficient plant crude extracts separations and metabolomics studies.

Arabidopsis↗

PubMed2006

Applications of metabolomics in agriculture.

Biological systems are exceedingly complex. The unraveling of the genome in plants and humans revealed fewer than the anticipated number of genes. Therefore, other processes such as the regulation of gene expression, the action of gene products, and the metabolic networks resulting from catalytic proteins must make fundamental contributions to the remarkable diversity inherent in living systems. Metabolomics is a relatively new approach aimed at improved understanding of these metabolic networks and the subsequent biochemical composition of plants and other biological organisms. Analytical tools within metabolomics including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy can profile the impact of time, stress, nutritional status, and environmental perturbation on hundreds of metabolites simultaneously resulting in massive, complex data sets. This information, in combination with transcriptomics and proteomics, has the potential to generate a more complete picture of the composition of food and feed products, to optimize crop trait development, and to enhance diet and health. Selected presentations from an American Chemical Society symposium held in March 2005 have been assembled to highlight the emerging application of metabolomics in agriculture.

Agriculture↗

PubMed2006

Pathway analysis of kidney cancer using proteomics and metabolic profiling.

BACKGROUND: Renal cell carcinoma (RCC) is the sixth leading cause of cancer death and is responsible for 11,000 deaths per year in the US. Approximately one-third of patients present with disease which is already metastatic and for which there is currently no adequate treatment, and no biofluid screening tests exist for RCC. In this study, we have undertaken a comprehensive proteomic analysis and subsequently a pathway and network approach to identify biological processes involved in clear cell RCC (ccRCC). We have used these data to investigate urinary markers of RCC which could be applied to high-risk patients, or to those being followed for recurrence, for early diagnosis and treatment, thereby substantially reducing mortality of this disease. RESULTS: Using 2-dimensional electrophoresis and mass spectrometric analysis, we identified 31 proteins which were differentially expressed with a high degree of significance in ccRCC as compared to adjacent non-malignant tissue, and we confirmed some of these by immunoblotting, immunohistochemistry, and comparison to published transcriptomic data. When evaluated by several pathway and biological process analysis programs, these proteins are demonstrated to be involved with a high degree of confidence (p values < 2.0 E-05) in glycolysis, propanoate metabolism, pyruvate metabolism, urea cycle and arginine/proline metabolism, as well as in the non-metabolic p53 and FAS pathways. In a pilot study using random urine samples from both ccRCC and control patients, we performed metabolic profiling and found that only sorbitol, a component of an alternative glycolysis pathway, is significantly elevated at 5.4-fold in RCC patients as compared to controls. CONCLUSION: Extensive pathway and network analysis allowed for the discovery of highly significant pathways from a set of clear cell RCC samples. Knowledge of activation of these processes will lead to novel assays identifying their proteomic and/or metabolomic signatures in biofluids of patient at high risk for this disease; we provide pilot data for such a urinary bioassay. Furthermore, we demonstrate how the knowledge of networks, processes, and pathways altered in kidney cancer may be used to influence the choice of optimal therapy.

Adult↗

PubMed2006

Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors.

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. We have used a metabolite profiling approach to test the hypothesis that quantitative signatures of primary metabolites can be used to characterize molecular changes in ovarian tumor tissues. Sixty-six invasive ovarian carcinomas and nine borderline tumors of the ovary were analyzed by gas chromatography/time-of-flight mass spectrometry (GC-TOF MS) using a novel contamination-free injector system. After automated mass spectral deconvolution, 291 metabolites were detected, of which 114 (39.1%) were annotated as known compounds. By t test statistics with P < 0.01, 51 metabolites were significantly different between borderline tumors and carcinomas, with a false discovery rate of 7.8%, estimated with repeated permutation analysis. Principal component analysis (PCA) revealed four principal components that were significantly different between both groups, with the highest significance found for the second component (P = 0.00000009). PCA as well as additional supervised predictive models allowed a separation of 88% of the borderline tumors from the carcinomas. Our study shows for the first time that large-scale metabolic profiling using GC-TOF MS is suitable for analysis of fresh frozen human tumor samples, and that there is a consistent and significant change in primary metabolism of ovarian tumors, which can be detected using multivariate statistical approaches. We conclude that metabolomics is a promising high-throughput, automated approach in addition to functional genomics and proteomics for analyses of molecular changes in malignant tumors.

Cluster Analysis↗

PubMed2006

Profiling at mRNA, protein, and metabolite levels reveals alterations in renal amino acid handling and glutathione metabolism in kidney tissue of Pept2-/- mice.

PEPT2 is an integral membrane protein in the apical membrane of renal epithelial cells that operates as a rheogenic transporter for di- and tripeptides and structurally related drugs. Its prime role is thought to be the reabsorption of filtered di- and tripeptides contributing to amino acid homeostasis. To elucidate the role of PEPT2 in renal amino acid metabolism we submitted kidney tissues of wild-type and a Pept2(-/-) mouse line to a comprehensive transcriptome, proteome and metabolome profiling and analyzed urinary amino acids and dipeptides. cDNA microarray analysis identified 147 differentially expressed transcripts in transporter-deficient animals, and proteome analysis by 2D-PAGE and MALDI-TOF-MS identified 37 differentially expressed proteins. Metabolite profiling by GC-MS revealed predominantly altered concentrations of amino acids and derivatives. Urinary excretion of amino acids demonstrated increased glycine and cysteine/cystine concentrations and dipeptides in urine were assessed by amino acid analysis of urine samples before and after in vitro dipeptidase digestion. Dipeptides constituted a noticeable fraction of urinary amino acids in Pept2(-/-) animals, only, and dipeptide-bound glycine and cystine were selectively increased in Pept2(-/-) urine samples. These findings were confirmed by a drastically increased excretion of cysteinyl-glycine (cys-gly). Urinary loss of cys-gly together with lower concentrations of cysteine, glycine, and oxoproline in kidney tissue and altered expression of mRNA and proteins involved in glutathione (GSH) metabolism suggests that PEPT2 is predominantly a system for reabsorption of cys-gly originating from GSH break-down, thus contributing to resynthesis of GSH.

Amino Acids↗

PubMed2006

Natural genetic variation of freezing tolerance in Arabidopsis.

Low temperature is a primary determinant of plant growth and survival. Using accessions of Arabidopsis (Arabidopsis thaliana) originating from Scandinavia to the Cape Verde Islands, we show that freezing tolerance of natural accessions correlates with habitat winter temperatures, identifying low temperature as an important selective pressure for Arabidopsis. Combined metabolite and transcript profiling show that during cold exposure, global changes of transcripts, but not of metabolites, correlate with the ability of Arabidopsis to cold acclimate. There are, however, metabolites and transcripts, including several transcription factors, that correlate with freezing tolerance, indicating regulatory pathways that may be of primary importance for this trait. These data identify that enhanced freezing tolerance is associated with the down-regulation of photosynthesis and hormonal responses and the induction of flavonoid metabolism, provide evidence for naturally increased nonacclimated freezing tolerance due to the constitutive activation of the C-repeat binding factors pathway, and identify candidate transcriptional regulators that correlate with freezing tolerance.

Acclimatization↗

PubMed2006

Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm.

BACKGROUND: Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept. RESULTS: High mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from http://fiehnlab.ucdavis.edu. A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae. CONCLUSION: More than 1.6 million molecular formulae in the range 0-500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution mass spectrometry), we conclude that a mass spectrometer capable of 3 ppm mass accuracy and 2% error for isotopic abundance patterns outperforms mass spectrometers with less than 1 ppm mass accuracy or even hypothetical mass spectrometers with 0.1 ppm mass accuracy that do not include isotope information in the calculation of molecular formulae.

Algorithms↗

PubMed2006

Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results.

Informatics standards and controlled vocabularies are essential for allowing information technology to help exchange, manage, interpret and compare large data collections. In a rapidly evolving field, the challenge is to work out how best to describe, but not prescribe, the use of these technologies and methods. A Metabolomics Standards Workshop was held by the US National Institutes of Health (NIH) to bring together multiple ongoing standards efforts in metabolomics with the NIH research community. The goals were to discuss metabolomics workflows (methods, technologies and data treatments) and the needs, challenges and potential approaches to developing a Metabolomics Standards Initiative that will help facilitate this rapidly growing field which has been a focus of the NIH roadmap effort. This report highlights specific aspects of what was presented and discussed at the 1st and 2nd August 2005 Metabolomics Standards Workshop.

Cell Physiological Phenomena↗

PubMed2006

Establishing reporting standards for metabolomic and metabonomic studies: a call for participation.

Metabolite concentrations in cellular systems are very much dependent on the physiological, environmental, and genetic status of an organism and are regarded as the ultimate result of cellular regulation, resulting in the visible phenotypes. Therefore, the comprehensive analysis of metabolite levels and fluxes renders a suitable tool for assessing the degree of perturbation in biological systems. Lessons derived from development of other OMICS areas (genomics, proteomics, and transcriptomics) have shown that large-scale comparisons and interpretations will require the re-use of data over long periods of time and by multiple laboratories with different expertise and backgrounds. Reaching this goal will require standardization of reporting structures of metabolomic studies for journal publication purposes, for regulatory deposition, and for database dissemination. An initiative by the Metabolomics Society is presented that aims to define important aspects of metabolomic workflows. These include biological study designs, chemical analysis, and data processing, as well as the ontologies that are necessary in this framework.

Animals↗

PubMed2006

Metabolite profiling in Arabidopsis.

Metabolite profiling is the multiparallel relative quantification of a mixture of compounds or compound classes using chromatography and universal detection technologies (GC-MS, LC-MS). In this respect it is an extension of classical single-target methods from which it can be distinguished by its broader view on profiling major biochemical events. This broader scope of analysis outweighs the disadvantages by making compromises in method development and the reduced accuracy for specific metabolites. This chapter exemplifies the strategies in metabolite profiling of polar compounds by gas chromatography-mass spectrometry (GC-MS). It gives experimental details on the basic steps: harvest, homogenization, extraction, fractionation, concentration, derivatization, data acquisition, raw data processing and result data tranformation.

Arabidopsis↗

PubMed2005

Metabolite profiling of Chlamydomonas reinhardtii under nutrient deprivation.

A metabolite profiling technique for Chlamydomonas reinhardtii cells for multiparallel analysis of low-molecular weight polar compounds was developed. The experimental protocol was optimized to quickly inactivate enzymatic activity, achieve maximum extraction capacity, and process large sample quantities. As a result of the rapid sampling, extraction, and analysis by gas chromatography coupled to time-of-flight mass spectrometry, more than 800 analytes from a single sample could be measured, of which more than 100 could be identified. Analyte responses could be determined mostly with ses less than 10%. Wild-type cells of C. reinhardtii strain CC-125 subjected to nitrogen-, phosphorus-, sulfur-, or iron-depleted growth conditions develop highly distinctive metabolite profiles. Individual metabolites undergo marked changes in their steady-state levels. Compared to control conditions, sulfur-depleted cells accumulated 4-hydroxyproline more than 50-fold, whereas the amount of 2-ketovaline was reduced to 2% of control levels. The contribution of each compound to the differences observed in the metabolic phenotypes is summarized in a quantitatively rigorous way by principal component analysis, which clearly discriminates the cells from different growth regimes and indicates that phosphorus-depleted conditions induce a deficiency syndrome quite different from the response to nitrogen, sulfur, or iron starvation.

Animals↗

PubMed2005

Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops.

There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome "fingerprinting" to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars.

Alkaloids↗

PubMed2005

Statistical methods for comparing comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry results: metabolomic analysis of mouse tissue extracts.

The potential for the utilization of GC x GC-time-of-flight (TOF) MS for high-resolution metabolomics studies is discussed, with the implementation of some statistical comparisons for biomarker detection. Metabolite profiles from NZO obese mice versus BL/6 control mice are compared and contrasted using a number of chromatogram comparison routines, including direct chromatogram comparisons, chromatogram subtraction and averaging routines, as well as a method for generating relative weighted peak surface difference chromatograms, and a more conventional Student's t-test statistical approach.

Animals↗

PubMed2005

Metabolic profiling of laser microdissected vascular bundles of Arabidopsis thaliana.

BACKGROUND: Laser microdissection is a useful tool for collecting tissue-specific samples or even single cells from animal and plant tissue sections. This technique has been successfully employed to study cell type-specific expression at the RNA, and more recently also at the protein level. However, metabolites were not amenable to analysis after laser microdissection, due to the procedures routinely applied for sample preparation. Using standard tissue fixation and embedding protocols to prepare histological sections, metabolites are either efficiently extracted by dehydrating solvents, or washed out by embedding agents. RESULTS: In this study, we used cryosectioning as an alternative method that preserves sufficient cellular structure while minimizing metabolite loss by excluding any solute exchange steps. Using this pre-treatment procedure, Arabidopsis thaliana stem sections were prepared for laser microdissection of vascular bundles. Collected samples were subsequently analyzed by gas chromatography-time of flight mass spectrometry (GC-TOF MS) to obtain metabolite profiles. From 100 collected vascular bundles (approximately 5,000 cells), 68 metabolites could be identified. More than half of the identified metabolites could be shown to be enriched or depleted in vascular bundles as compared to the surrounding tissues. CONCLUSION: This study uses the example of vascular bundles to demonstrate for the first time that it is possible to analyze a comprehensive set of metabolites from laser microdissected samples at a tissue-specific level, given that a suitable sample preparation procedure is used.

Journal Article↗

PubMed2005

Systems rebalancing of metabolism in response to sulfur deprivation, as revealed by metabolome analysis of Arabidopsis plants.

Sulfur is an essential macro-element in plant and animal nutrition. Plants assimilate inorganic sulfate into two sulfur-containing amino acids, cysteine and methionine. Low supply of sulfate leads to decreased sulfur pools within plant tissues. As sulfur-related metabolites represent an integral part of plant metabolism with multiple interactions, sulfur deficiency stress induces a number of adaptive responses, which must be coordinated. To reveal the coordinating network of adaptations to sulfur deficiency, metabolite profiling of Arabidopsis has been undertaken. Gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry techniques revealed the response patterns of 6,023 peaks of nonredundant ion traces and relative concentration levels of 134 nonredundant compounds of known chemical structure. Here, we provide a catalogue of the detected metabolic changes and reconstruct the coordinating network of their mutual influences. The observed decrease in biomass, as well as in levels of proteins, chlorophylls, and total RNA, gives evidence for a general reduction of metabolic activity under conditions of depleted sulfur supply. This is achieved by a systemic adjustment of metabolism involving the major metabolic pathways. Sulfur/carbon/nitrogen are partitioned by accumulation of metabolites along the pathway O-acetylserine to serine to glycine, and are further channeled together with the nitrogen-rich compound glutamine into allantoin. Mutual influences between sulfur assimilation, nitrogen imbalance, lipid breakdown, purine metabolism, and enhanced photorespiration associated with sulfur-deficiency stress are revealed in this study. These responses may be assembled into a global scheme of metabolic regulation induced by sulfur nutritional stress, which optimizes resources for seed production.

Amino Acids↗