Biology subjects

Wretlind, A.

Publications and source records attributed to Wretlind, A..

4 recordsLinked to original sources

bioRxiv2023

Development of UHPLC MS/MS method for determination and quantification of endocannabinoids in cerebrospinal fluid.

N-acylethanolamines (NAEs) and primary fatty amides (PFAMs) are of a great interest due to the range of physiological effects they exhibit, potentially serving as neuromodulators. However, they are present at nano and picomolar concentrations in human cerebrospinal fluid (CSF) samples, posing challenges for detection and measurement using conventional Ultra-high performance liquid chromatography systems coupled to tandem mass spectrometry (UHPLC-MS). UHPLC-MS was used in dynamic multiple reaction monitoring (dMRM) mode. Seven deuterated NAEs internal standards were used to develop the method. Six solvent combinations were tested for extraction efficiency, accuracy, precision, matrix effect, linearity, limits of detection. Lastly the method was applied to CSF from healthy individuals (n=33) to estimate their natural range of concentrations. Extraction with acetonitrile/acetone showed the highest efficiency and recovery. The presented method was able to measure the following 17 NAEs and PFAMs in human CSF: linoleoyl ethanolamide, heptadecanoyl ethanolamide, stearoyl ethanolamide, palmitoyl ethanolamide, dihomolinolenoyl ethanolamide, eicosatrienoic acid ethanolamide, behenamide, octadecanamide, lauramide, tetradecanamide, erucamide, linoleamide, palmitamide, myristic monoethanolamide, pentadecanoyl ethanolamide, oleamide and palmitoleoyl ethanolamide. In healthy individuals the concentrations ranged three-fold from pg/mL to mg/mL. Further studies could apply this method to clinical CSF samples.

biochemistry↗

bioRxiv2022

Human cerebrospinal fluid sample preparation and annotation for integrated lipidomics and metabolomics profiling studies

ObjectiveMass spectrometry (MS)-based lipidomics and metabolomics approaches play an essential role in identifying molecular profiles and relevant clinical biomarkers associated with diseases. Cerebrospinal fluid (CSF) is a metabolically diverse biofluid and a key specimen for exploring biochemical changes in neurodegenerative diseases because its composition reflects brain metabolic activity. CSF lipidomics is receiving increasing attention owing to the importance of lipids in brain molecular signaling and their association with several neurological diseases. Detecting lipid species in CSF using MS-based techniques remains challenging because lipids are highly complex in structure and their concentrations span over a broad dynamic range. This work aimed to develop a robust lipidomics and metabolomics method based on commonly used two-phase extraction systems from human CSF samples. MethodsPrioritizing lipid detection, biphasic extraction methods, Folch, Bligh & Dyer (B&D), Matyash and acidified Folch and B&D (aFolch and aB&D), were compared using 150 l of human CSF samples (n=6) for the simultaneous extraction of lipids and metabolites with a wide range of polarity in a single extraction. Multiple chromatographical separation approaches, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), and gas chromatography (GC), were utilized to characterize human CSF metabolome through MS-based untargeted approaches. ResultsA total of 219 lipids across 12 lipid subclasses were identified in CSF samples using RPLC-MS/MS. The aB&D method was found as the most reproducible technique (RSD <15%) for lipid extraction. We found remarkable differences in extraction efficiencies among the five different procedures. The aB&D and B&D yielded the highest peak intensities for targeted lipid internal standards and displayed superior extracting power for major endogenous lipid classes. A total of 674 unique metabolites with a wide polarity range were annotated in CSF using, combining RPLC-MS/MS (n=219), HILIC-MS/MS (n=304) and GC-QTOF MS (n=151). ConclusionsOverall, our findings show that the aB&D extraction method provided suitable lipid coverage, reproducibility, and extraction efficiency for global lipidomics profiling of human CSF samples. In combination with RPLC-MS/MS lipidomics, complementary screening approaches enabled a comprehensive metabolite signature that can be employed in an array of clinical studies.

biochemistry↗

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

Integrated lipidomics and proteomics network analysis highlights lipid and immunity pathways associated with Alzheimer's disease

INTRODUCTIONThere is an urgent need to understand the molecular mechanisms underlying Alzheimers Disease (AD) to enable early diagnosis and develop effective treatments. Here we aim to investigate Alzheimers dementia using an unsupervised lipid, protein and gene multi-omic integrative approach. METHODSA lipidomics dataset (185 AD, 40 MCI and 185 controls) and a proteomics dataset (201 AD patients, 104 MCI individuals and 97 controls) were utilised for weighted gene co-expression network analyses (WGCNA). An additional proteomics dataset (94 AD, 55 MCI and 100 controls) was included for external proteomics validation. Modules created within each modality were correlated with clinical AD diagnosis, brain atrophy measures and disease progression, as well as with each other. Gene Ontology (GO) enrichment analysis was employed to examine the biological processes and molecular and cellular functions for protein modules associated with AD phenotypes. Lipid species were annotated in the lipid modules associated with AD phenotypes. Associations between established AD risk loci and lipid/protein modules that showed high correlation with AD phenotypes were also explored. RESULTSFive of the 20 identified lipid modules and five of the 17 identified protein modules were correlated with AD phenotypes. Lipid modules comprising of phospholipids, triglycerides, sphingolipids and cholesterol esters, correlated with AD risk loci involved in immune response and lipid metabolism. Five protein modules involved in positive regulation of cytokine production, neutrophil mediated immunity, humoral immune responses were correlated with AD risk loci involved in immune and complement systems. DISCUSSIONWe have shown the first multi-omic study linking genes, proteins and lipids to study pathway dysregulation in AD. Results identified modules of tightly regulated lipids and proteins that were strongly associated with AD phenotypes and could be pathology drivers in lipid homeostasis and innate immunity. Research in ContextO_LILipid and protein modules were preserved amongst Alzheimers disease (AD) patients, participants with mild cognitive impairment (MCI) and controls. Protein modules were also externally validated. C_LIO_LIFive lipid and five protein modules out of a total of thirty-seven correlated with clinical AD diagnosis, brain atrophy measurements and the rate of cognitive decline in AD. C_LIO_LILipid and protein modules associated with AD phenotypes showed associations with established AD risk loci involved in lipid and immune pathways. C_LI

neuroscience↗