Biology subjects

Kindt, A.

Publications and source records attributed to Kindt, A..

6 recordsLinked to original sources

bioRxiv2025

Longitudinal Metabolomic Profiling of Biogenic Amines in Plasma and CSF, and Their Correlation, Reveals Sex-Specific and Age Changes in TgF344 Alzheimer's Disease Transgenic and Wildtype Rats

BackgroundAlterations in amine metabolism have been implicated Alzheimers disease (AD). Cerebrospinal fluid (CSF) and plasma are key biofluids in AD research. CSF is considered to better reflect brain metabolic alterations than plasma, while plasma can be obtained more easily. However, plasma-CSF relationships are unclear. AimTo investigate longitudinal changes of amines in plasma and CSF, and their correlation across the two, in male and female TgF344 AD transgenic versus wildtype (WT) rats. MethodLC-MS-based targeted metabolomics was used to analyze 60 and 55 amines in plasma and CSF, respectively, of male and female TgF344-AD and WT rats, at 12, 25, 50 and 85 weeks. Statistical analysis was performed using generalized logistic regressions, Pearson correlations, and differential correlations between groups and matrixes. ResultsCompared to WT controls, at 12 weeks, TgF344-AD rats showed an increase of 3-methylhistidine, anserine, cysteine, s-methylcysteine, while at 25 weeks, male TgF344-AD rats showed pronounced increases in CSF levels of alpha-aminobutyric acid, asparagine, glycylglycine, glycylproline, histidine, isoleucine, kynurenine, leucine, methionine, methionine sulfone, norepinephrine, phenylalanine, proline, tyrosine, and valine. At 50 weeks, female TgF344-AD rats exhibited reductions in CSF for DL-3-aminoisobutyric acid, gamma-aminobutyric acid, ornithine, and putrescine. Distinct plasma-CSF correlations were found for 1-methylhistidine, 2-aminoadipic acid, putrescine, kynurenine, N6,N6,N6-trimethyl-lysine, DL-3-aminoisobutyric acid, and taurine, particularly in TgF344-AD rats. ConclusionsBody fluid, age- and sex-dependent amine alterations in CSF and plasma of TgF344-AD rats compared to WT controls provide important insights into AD disease processes and may aid early diagnosis and therapeutic targeting.

systems biology↗

bioRxiv2025

Metabolic Signatures of Pulmonary Embolism in COVID-19: Insights from Longitudinal Intensive Care Unit Profiles

ABSTRACTO_ST_ABSBackgroundC_ST_ABSPulmonary embolism is a severe complication of COVID-19 infection, associated with a hypercoagulable state and heightened risk of blood clots. As SARS-CoV-2 has become endemic, understanding pulmonary embolisms metabolic effects in COVID-19 patients is warranted. This study investigated the longitudinal metabolic profiles of 66 Intensive Care Unit-admitted COVID-19 patients at Erasmus Medical Center to identify metabolites and mechanisms associated with pulmonary embolism. MethodA total of 1209 metabolic species were measured, including amines and lipids. Metabolic changes were analysed across four timeframes: i) general analysis of pulmonary embolism, ii) 72 hours prior to pulmonary embolism, iii) 48 hours prior and the day of pulmonary embolism, and iv) the day of and 48 hours post-pulmonary embolism. ResultsThe general analysis revealed significant upregulation of amines, triglycerides, phosphatidylethanolamines, ether-linked phosphatidylethanolamines, and eicosanoids in patients who developed a pulmonary embolism. Phosphatidylethanolamines containing the 20:3 fatty acid side chain were notably elevated. Minimal metabolic dysregulation was observed 72 hours before pulmonary embolism, with subtle increases in lysophosphatidylcholines and lysophosphatidylethanolamines. In contrast, there was a strong metabolic response during and post-pulmonary embolism, phosphatidylethanolamines (47%), ether-linked phosphatidylethanolamines(96%) and sphingosines(40%). ConclusionThese findings underscore the critical role of lipid metabolism in pulmonary embolism, particularly triglycerides and specific lipid species. The limited metabolic perturbations before pulmonary embolism suggest early prediction challenges, emphasising the need for further research into temporal metabolic changes and their clinical applications.

molecular biology↗

bioRxiv2025

Metabolic Alteration in Oxylipins and Endocannabinoids Point to an Important Role for Soluble Epoxide Hydrolase and Inflammation in Alzheimer's Disease - Finding from Alzheimer's Disease Neuroimaging Initiative.

Mounting evidence implicates inflammation as a key factor in Alzheimers disease (AD) development. We previously identified pro-inflammatory soluble epoxide hydrolase (sEH) metabolites to be elevated in plasma and CSF of AD patients and to be associated with lower cognition in non-AD subjects. Soluble epoxide hydrolase is a key enzyme converting anti-inflammatory epoxy fatty acids to pro-inflammatory diols, reported to be elevated in multiple cardiometabolic disorders. Here we analyzed over 700 fasting plasma samples from the baseline of Alzheimers Disease Neuroimaging Initiative (ADNI) 2/GO study. We applied targeted mass spectrometry method to provide absolute quantifications of over 150 metabolites from oxylipin and endocannabinoids pathway, interrogating the role for inflammation/immune dysregulation and the key enzyme soluble epoxide hydrolase in AD. We provide further insights into the regulation of this pathway in different disease stages, APOE genotypes and between sexes. Additionally, we investigated in mild cognitive impaired (MCI) patients, metabolic signatures that inform about resilience to progression and conversion to AD. Key findings include I) confirmed disruption in this key central pathway of inflammation and pointed to dysregulation of sEH in AD with sex and disease stage differences; II) identified markers of disease progression and cognitive resilience using sex and ApoE genotype stratified analysis highlighting an important role for bile acids, lipid peroxidation and stress response hormone cortisol. In conclusion, we provide molecular insights into a central pathway of inflammation and links to cognitive dysfunction, suggesting novel therapeutic approaches that are based on targeting inflammation tailored for subgroups of individuals based on their sex, APOE genotype and their metabolic profile.

neuroscience↗

bioRxiv2025

Lipidomic Fingerprints Reveal Sex-, Age-, and Disease-Dependent Differences in the TgF344-AD Transgenic Rats

BackgroundGathering information on Alzheimers disease (AD) progression in human poses significant challenges due to the lengthy timelines and ethical considerations involved. Animal AD models provide a valuable alternative for conducting mechanistic studies and testing potential therapeutic strategies. Disturbed lipid homeostasis is among the earliest neuropathological features of AD. AimTo identify longitudinal plasma lipidomic changes associated with age, sex, and AD in male and female TgF344-AD and wild-type rats. MethodsA total of 751 lipids in 141 rats (n=73 TgF344-AD; n= 68 WT) were quantified at 12, 25, 50, and 85 weeks). Differential abundances of lipids were assessed using generalized logical regression models, correcting for i) age and sex, for ii) individual age groups, and iii) sex-specific differences. Predictive lipid signature models for AD were developed using stepwise feature selection for the full age range, as well as for midlife. ResultsSex differences were identified among all ages in sphingomyelin (SM), phosphatidylcholine (PC), and phosphatidylethanolamine (PE) lipid classes. AD and age-related differences were found in the SM class in mid-life (25-50 weeks). Other AD and age-related differences were found in the ratios of linoleic acid and 5 of its products. Moreover, similarities in lipidomic profile changes were observed for humans and rats. The full age range and mid-life predictive lipid signatures for AD resulted in an AUC of 0.75 and 0.68, respectively. ConclusionsOur findings highlight the value of lipidomic in identifying early AD-related lipid alterations, offering a promising avenue for understanding disease mechanisms and advancing biomarker discovery.

systems biology↗

bioRxiv2025

mzQuality: A tool for quality monitoring and reporting of targeted mass spectrometry measurements

Analyzing metabolites using mass spectrometry can offer valuable insight into an individuals health or disease status. However, various sources of experimental variation can affect the data, making robust quality control essential. In this context, we introduce mzQuality, a user-friendly software tool designed to evaluate and correct technical variations in mass spectrometry-based metabolomics data. MzQuality offers key quality control features, such as batch correction, outlier identification, and analysis of signal-to-noise ratios. It supports any peak-integrated processed data independent of vendor software and does not require the user to have any programming skills. We demonstrate the functionality of mzQuality with a data set of 419 samples measured across six batches, in which mzQuality effectively minimized experimental variation, ensuring the datas readiness for statistical analysis and biological interpretation. With customizable settings, mzQuality can be seamlessly integrated into research workflows to produce more accurate and reproducible metabolomics data.

bioinformatics↗

bioRxiv2024

Normalization strategies for lipidome data in cell line panels

Sample collection can significantly affect measurements of relative lipid concentrations in cell line panels, hiding intrinsic biological properties of interest between cell lines. Most quality control steps in lipidomic data analysis focus on controlling technical variation. Correcting for the total amount of biological material remains an additional challenge for cell line panels. Here, we investigated how we can normalize lipidomic data acquired from multiple cell lines to correct for differences in sample biomass. We studied how commonly used data normalization and transformation steps during analysis influenced the resulting lipid data distributions. We compared normalization by biological properties such as cell count or total protein concentration, to statistical and data-based approaches, such as median, mean, or probabilistic quotient-based normalization and used intraclass correlation to estimate how similarity between replicates changed after normalization. Normalizing lipidomic data by cell count improved similarity between replicates, but only for a study with cell lines with similar morphological phenotypes. For cell line panels with multiple morphologies collected over a longer time, neither cell count nor protein concentration was sufficient to increase the similarity of lipid abundances between replicates of the same cell line. Data-based normalizations increased these similarities, but also created artifacts in the data caused by a bias towards the large and variable lipid class of triglycerides. This artifact was reduced by normalizing for the abundance of only structural lipids. We conclude that there is a delicate balance between improving the similarity between replicates and avoiding artifacts in lipidomic data and emphasize the importance of an appropriate normalization strategy in studying biological phenomena using lipidomics.

bioinformatics↗