Biology subjects

Suvitaival, T.

Publications and source records attributed to Suvitaival, T..

6 recordsLinked to original sources

bioRxiv2021

High-throughput UHPLC-MS to screen metabolites in feces for gut metabolic health

(1) BackgroundFeces are the product of our diets and have been linked to diseases of the gut, including Chrons disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. (2) MethodsAs sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and non-polar metabolites. Fecal samples (n= 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. (3) ResultsMS analyses of the prepared fecal samples have shown reproducibility and coverage of n=28 metabolites, mostly comprising bile acids and amino acids. We report metabolite-wise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD, LOQ and range of linearity. The average concen-trations for 135 healthy participants are reported here for clinical applications. (4) Conclusionsour high-throughput method provides an efficient tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

biochemistry↗

bioRxiv2020

Understanding the Lipidome at the Systems Level with lipidomeR

Lipidomics is one of the fastest-growing areas of molecular profiling in medicine. While increasing amounts of lipidomics data are generated, tools for analyzing and interpreting these data are not equally widely available. We present the lipidomeR -- a tool specifically designed for systematic interpretation of large lipidome-wide studies. The lipidomeR binds together statistical analysis and high-dimensional visualization, providing a reproducible pipeline for rapid interpretation of the lipidome via integrative publication-ready figures. The lipidomeR package is available through the Comprehensive R Archive (CRAN). We demonstrate the lipidomeR with three studies from the Metabolomics Workbench repository, ranging from the human plasma reference material to breast tumor tissue and to the progression of non-alcoholic liver disease (NAFLD) in the liver. In these studies, lipidomeR reveals a diversity of lipidomic patterns, both, within and between the lipid classes as well as over the stages of progression of the diseases.

bioinformatics↗

bioRxiv2020

Lipidomics of human adipose tissue reveals diversity between body areas

Background and aimsAdipose tissue plays a pivotal role in storing excess fat and its composition reflects the history of persons lifestyle and metabolic health. Broad profiling of lipids with mass spectrometry has potential for uncovering new knowledge on the pathology of obesity, metabolic syndrome, diabetes and other related conditions. Here, we developed a lipidomic method for analyzing human subcutaneous adipose biopsies. We applied the method to four body areas to understand the differences in lipid composition between these areas. Materials and methodsAdipose tissue biopsies from 10 participants were analyzed using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The method development included the optimization of the lipid extraction, the sample amount and the sample dilution factor to detect lipids in an appropriate concentration range. Lipidomic analyses were performed for adipose tissue collected from the abdomen, breast, thigh and lower back. Differences in lipid levels between tissues were visualized with heatmaps. ResultsLipidomic analysis on human adipose biopsies lead to the identification of 187 lipids in 2 mg of sample. Technical variation of the lipid-class specific internal standards were below 5 %, thus indicating acceptable repeatability. Triacylglycerols were highly represented in the adipose tissue samples, and lipids from 13 lipid classes were identified. Long polyunsaturated triacylglycerols in higher levels in thigh (q<0.05), when compared with the abdomen, breast and lower back, indicating that the lipidome was area-specific. ConclusionThe method presented here is suitable for the analysis of lipid profiles in 2 mg of adipose tissue. The amount of fat across the body is important for health but we argue that also the distribution and the particular profile of the lipidome may be relevant for metabolic outcomes. We suggest that the method presented in this paper could be useful for detecting such aberrations.

biophysics↗

bioRxiv2019

Describing the fecal metabolome in cryogenically collected samples from healthy participants.

IntroductionThe chemical composition of feces plays an important role in human metabolism. Metabolomics and lipidomics are valuable tools for screening the metabolite composition in feces. Here we set out to describe fecal metabolite composition in healthy participants in frozen stools.\n\nMethodsFrozen stool samples were collected from 10 healthy volunteers and cryogenically drilled in four areas along the specimen. Polar metabolites were analyzed using derivatization followed by two-dimensional gas chromatography and time of flight mass spectrometry. Lipids were detected using ultra high-performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry. The technical variation threshold was set to 30% in pooled quality control samples and metabolite variation was then assessed in four areas per specimen. A data-generated network using metabolites found in all areas was computed for healthy participants.\n\nResults2326 metabolic features were detected. Out of a total of 298 metabolites that were annotated we report here 185 that showed a technical variation of x< 30%. These metabolites included amino acids, fatty acid derivatives, carboxylic acids and phenolic compounds. Lipids predominantly belonged to the groups of diacylglycerols, triacylglycerols and ceramides. Metabolites varied between sampling areas (14%-80%). A network using metabolites present in all areas showed two main clusters, DAG lipids and phenyllactic acid.\n\nConclusionsIn feces from healthy participants, the main groups detected were phenolic compounds, ceramides, diacylglycerols and triacylglycerols. Metabolite levels differed considerably depending on the sampling area.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=52 SRC=\"FIGDIR/small/708685v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (16K):\norg.highwire.dtl.DTLVardef@17db665org.highwire.dtl.DTLVardef@51e140org.highwire.dtl.DTLVardef@19b55d7org.highwire.dtl.DTLVardef@120d399_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2019

Targeted Clinical Metabolomics Platform for the Stratification of Diabetic Patients

BackgroundSeveral small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities and complications. Here, we report the development and validation of a novel, quantitative, analytical method for use in the diabetes clinic. This method enables the determination of a selected panel of 36 metabolite biomarkers from human plasma.\n\nMethodsBased on a review of the literature and our own data, we selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings.\n\nResultsWe validated the method in terms of limit of (a) detection (LOD), (b) limit of quantitation (LOQ), (c) linearity (R2), (d) linear range, and (e) intra- and inter-day repeatability of each metabolite. The methods performance was demonstrated in the analysis of selected samples from a diabetes cohort study. Metabolite levels were associated with clinical measurements and kidney complications in type 1 diabetes (T1D) patients. Specifically, both amino acids and amino acid-related analytes were associated with macro-albuminuria. Additionally, specific bile acids were associated with kidney function, anti-hypertensive medication, statin medication and clinical lipid measurements.\n\nConclusionsThe developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.

biochemistry↗

bioRxiv2018

Molecular atlas of postnatal mouse heart development

RationaleMammals lose the ability to regenerate their hearts within one week after birth. During this regenerative window, cardiac energy metabolism shifts from glycolysis to fatty acid oxidation, and recent evidence suggests that metabolism may participate in controlling cardiomyocyte cell cycle. However, the molecular mechanisms mediating the loss of postnatal cardiac regeneration are not fully understood.\n\nObjectiveThis study aims at providing an integrated resource of mRNA, protein and metabolite changes in the neonatal heart to identify metabolism-related mechanisms associated with the postnatal loss of regenerative capacity.\n\nMethods and ResultsMouse ventricular tissue samples taken on postnatal days 1, 4, 9 and 23 (P01, P04, P09 and P23, respectively) were analyzed with RNA sequencing (RNAseq) and global proteomics and metabolomics. Differential expression was observed for 8547 mRNAs and for 1199 of the 2285 quantified proteins. Furthermore, 151 metabolites with significant changes were identified. Gene ontology analysis, KEGG pathway analysis and fuzzy c-means clustering were used to identify biological processes and metabolic pathways either up- or downregulated on all three levels. Among these were branched chain amino acid degradation (upregulated at P23) and production of free saturated and monounsaturated medium- to long-chain fatty acids (upregulated at P04 and P09; downregulated at P23). Moreover, the HMG-CoA synthase (HMGCS)-mediated mevalonate pathway and ketogenesis were transiently activated. Pharmacological inhibition of HMGCS in primary neonatal rat ventricular cardiomyocytes reduced the percentage of BrdU+ cardiomyocytes, providing evidence that the mevalonate and ketogenesis routes may participate in regulating cardiomyocyte cell cycle.\n\nConclusionsThis is the first systems-level resource combining data from genome-wide transcriptomics with global quantitative proteomics and untargeted metabolomics analyses of the mouse heart throughout the early postnatal period. This integrated multi-level data of molecular changes associated with the loss of cardiac regeneration may open up new possibilities for the development of regenerative therapies.

systems biology↗